update

USER/SPF/03hezhong/01BatSoh/BatParam.py

@@ -1,132 +0,0 @@
-
-#定义电池参数
-from types import CellType
-import sys
-
-class BatParam:
-
-    def __init__(self,celltype):
-
-        # if 'PK500' in sn:
-        #     self.celltype=1 #6040三元电芯
-        # elif 'PK502' in sn:
-        #     self.celltype=2 #4840三元电芯
-        # elif 'PK504' in sn:
-        #     self.celltype=99    #60ah林磷酸铁锂电芯
-        # elif 'MGMLXN750' in sn:
-        #     self.celltype=3 #力信50ah三元电芯
-        # elif 'MGMCLN750' in sn: 
-        #     self.celltype=4 #CATL 50ah三元电芯
-        # else:
-        #     print('未找到对应电池编号！！！')
-        #     sys.exit()
-
-        if celltype==1: #6040
-            self.Capacity = 41
-            self.PackFullChrgVolt=69.99
-            self.CellFullChrgVolt=4.2
-            self.CellVoltNums=17
-            self.CellTempNums=4
-            self.FullChrgSoc=98
-            self.PeakSoc=57
-            self.PackCrntDec=1
-            self.LookTab_SOC = [0,	    3.5348,	8.3581,	13.181,	18.004,	22.827,	27.651,	32.474,	37.297,	42.120,	46.944,	51.767,	56.590,	61.413,	66.237,	71.060,	75.883,	80.707,	85.530,	90.353,	95.176,	100,   105]
-            self.LookTab_OCV = [3.3159,	3.4384,	3.4774,	3.5156,	3.5478,	3.5748,	3.6058,	3.6238,	3.638,	3.6535,	3.6715,	3.6951,	3.7279,	3.7757,	3.8126,	3.8529,	3.8969,	3.9446,	3.9946,	4.0491,	4.109,	4.183, 4.263]
-        
-        elif celltype==2: #4840
-            self.Capacity = 41
-            self.PackFullChrgVolt=69.99
-            self.CellFullChrgVolt=4.2
-            self.CellVoltNums=14
-            self.CellTempNums=4
-            self.FullChrgSoc=98
-            self.PeakSoc=57
-            self.PackCrntDec=1
-            self.LookTab_SOC = [0,	    3.5348,	8.3581,	13.181,	18.004,	22.827,	27.651,	32.474,	37.297,	42.120,	46.944,	51.767,	56.590,	61.413,	66.237,	71.060,	75.883,	80.707,	85.530,	90.353,	95.176,	100,   105]
-            self.LookTab_OCV = [3.3159,	3.4384,	3.4774,	3.5156,	3.5478,	3.5748,	3.6058,	3.6238,	3.638,	3.6535,	3.6715,	3.6951,	3.7279,	3.7757,	3.8126,	3.8529,	3.8969,	3.9446,	3.9946,	4.0491,	4.109,	4.183, 4.263]
-        
-        elif celltype==3:   #力信50ah三元电芯
-            self.Capacity = 51
-            self.PackFullChrgVolt=80
-            self.CellFullChrgVolt=4.2
-            self.CellVoltNums=20
-            self.CellTempNums=4
-            self.FullChrgSoc=98
-            self.PeakSoc=57
-            self.PackCrntDec=1
-            self.LookTab_SOC = [0,	    5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100,   105]
-            self.LookTab_OCV = [3.357, 	3.455, 	3.493, 	3.540, 	3.577, 	3.605, 	3.622, 	3.638, 	3.655, 	3.677, 	3.707, 	3.757, 	3.815, 	3.866, 	3.920, 	3.976, 	4.036, 	4.099, 	4.166, 	4.237, 	4.325, 4.415]
-        
-        elif celltype==4:   #CATL 50ah三元电芯
-            self.Capacity = 50
-            self.PackFullChrgVolt=80
-            self.CellFullChrgVolt=4.2
-            self.CellVoltNums=20
-            self.CellTempNums=2
-            self.FullChrgSoc=98
-            self.PeakSoc=57
-            self.PackCrntDec=-1
-            self.LookTab_SOC = [0,	    5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100,   105]
-            self.LookTab_OCV = [3.152, 	3.397, 	3.438, 	3.481, 	3.523, 	3.560, 	3.586, 	3.604, 	3.620, 	3.638, 	3.661, 	3.693, 	3.748, 	3.803, 	3.853, 	3.903, 	3.953, 	4.006, 	4.063, 	4.121, 	4.183, 4.253]
-        
-        elif celltype==99:   #60ah磷酸铁锂电芯
-            self.Capacity = 103
-            self.PackFullChrgVolt=69.99
-            self.CellFullChrgVolt=3.5
-            self.OcvInflexionBelow=3.285
-            self.OcvInflexion2=3.296
-            self.OcvInflexion3=3.328
-            self.OcvInflexionAbove=3.4
-            self.CellVoltNums=20
-            self.CellTempNums=4
-            self.FullChrgSoc=98
-            self.PeakSoc=59
-            self.PeakVoltLowLmt=3.35
-            self.PeakVoltUpLmt=3.4
-            self.PeakCellVolt=[3.362,3.363,3.365,3.366,3.367]
-            self.PackCrntDec=1
-            self.BalCurrent=0.015
-            self.LookTab_SOC = [0.00, 	2.40, 	6.38, 	10.37, 	14.35, 	18.33, 	22.32, 	26.30, 	30.28, 	35.26, 	40.24, 	45.22, 	50.20, 	54.19, 	58.17, 	60.16, 	65.14, 	70.12, 	75.10, 	80.08, 	84.06, 	88.05, 	92.03, 	96.02, 	100.00]
-            self.LookTab_OCV = [2.7151,	3.0298,	3.1935,	3.2009,	3.2167,	3.2393,	3.2561,	3.2703,	3.2843,	3.2871,	3.2874,	3.2868,	3.2896,	3.2917,	3.2967,	3.3128,	3.3283,	3.3286,	3.3287,	3.3288,	3.3289,	3.3296,	3.3302,	3.3314,	3.3429]
-        
-        elif celltype==100:   #CATL 102ah三元电芯
-            self.Capacity = 102
-            self.PackFullChrgVolt=80
-            self.CellFullChrgVolt=4.2
-            self.CellVoltNums=96
-            self.CellTempNums=16
-            self.FullChrgSoc=98
-            self.PeakSoc=57
-            self.PackCrntDec=1
-            self.LookTab_SOC = [0,	    5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100]
-            self.LookTab_OCV = [3.397,	3.429,	3.462,	3.507,	3.551,	3.575,	3.600,	3.617,	3.633,	3.658,	3.683,	3.735,	3.786,	3.836,	3.887,	3.939,	3.992,	4.053,	4.113,	4.195,	4.277]
-
-            self.CellTempHighLv1=45
-            self.CellTempHighLv2=50
-            self.CellTempLowLv1=-20
-            self.CellTempLowLv2=-25
-            self.CellTempDiffLv1=10
-            self.CellTempDiffLv2=15
-            self.CellTempRate=0.99
-
-            self.CellOvLv1=4.3
-            self.CellOvLv2=4.35
-            self.CellUvLv1=2.8
-            self.CellUvLv2=2.5
-            self.CellVoltDiffLv1=0.3
-            self.CellVoltDiffLv2=0.5
-            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
-            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
-            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
-            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
-
-            self.PackChgOc=1.5*self.Capacity
-            self.PackDisChgOc=3*self.Capacity
-
-            self.SocJump=3
-            self.SocClamp=3
-
-        else:
-            print('未找到对应电池编号！！！')
-            # sys.exit()
-

USER/SPF/alibaba/01BatSoh/BatParam.py

@@ -0,0 +1,196 @@
+
+#定义电池参数
+
+class BatParam:
+    def __init__(self,celltype):
+
+        #公用参数................................................................................................................................................
+        #热失控参数
+        self.TrwTempHigh=60
+        self.TrwTempRate=3
+        self.TrwTempDiff=15
+        self.TrwCellVoltDiff=2.5
+        self.TrwCellVoltFall=0.5
+        self.TrwCellVoltLow=1.5
+        self.TrwPackVoltFall=1.5
+        
+        #故障诊断参数
+        self.temp_time=10
+        self.volt_time=10
+
+        self.SocJump=10
+        self.SocClamp=0.1
+        self.SocLow=3
+        self.SocDiff=20
+
+        self.SohLow=65
+        self.SohDiff=15
+
+        #mana-kendall趋势检验参数
+        self.mk_p=0.001
+        self.mk_z=-5
+        self.mk_Tau=-0.7
+        self.mk_slope=-0.1
+        self.mk_s=-260
+        self.mk_svar=1600
+
+        self.OcvWeight_Temp=[-30,-20,-10,0,10,20,30,40,50]
+        self.OcvWeight_StandingTime=[0,500,600,1200,1800,3600,7200,10800]
+        self.OcvWeight            =[[0,0,  0,  0,    0,   0.1,0.3, 1],
+                                    [0,0,  0,  0,    0,   0.1,0.3, 1],
+                                    [0,0,  0,  0,    0,   0.2,0.5, 1],
+                                    [0,0,  0,  0,    0.2, 0.4,0.8, 1],
+                                    [0,0,  0,  0.1,  0.3, 0.6,1,   1],
+                                    [0,0,  0.1,0.2,  0.5, 0.8,1,   1],
+                                    [0,0,  0.1,0.3,  0.6, 1,  1,   1],
+                                    [0,0,  0.1,0.3,  0.7, 1,  1,   1],
+                                    [0,0,  0.2,0.3,  0.8, 1,  1,   1]]
+
+
+        if celltype==1: #永联+康普顿21700
+            self.Capacity = 4*2
+            self.PackFullChrgVolt=69.99
+            self.CellFullChrgVolt=4.2
+            self.CellFullChrgCrnt=-4
+            self.CellVoltNums=120
+            self.CellTempNums=40
+            self.FullChrgSoc=90
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+            self.LookTab_SOC = [0,	     5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100]
+            self.LookTab_OCV = [2.8497,	3.0924,	3.2868,	3.4117,	3.4461,	3.5032,	3.5602,	3.6049,	3.6368,	3.6709,	3.7121,	3.7593,	3.8237,	3.8755,	3.9093,	3.9518,	4.0119,	4.0615,	4.077,	4.0969,	4.1821]
+
+            self.CellOvLv1=4.2
+            self.CellOvLv2=4.25
+            self.CellUvLv1=2.8
+            self.CellUvLv2=2.5
+            self.CellVoltDiffLv1=0.3
+            self.CellVoltDiffLv2=0.5
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=10
+            self.CellTempDiffLv2=15
+            self.CellTempRate=5
+
+            self.PackChgOc=-40
+            self.PackDisOc=200
+
+            self.LeakCurrentLv1=-10
+            self.LeakCurrentLv2=-15
+            self.LeakCurrentLv3=-50
+
+            self.TrwVoltRate=-1
+
+        elif celltype==2: #中恒+华庭18650
+            self.Capacity = 2*9
+            self.CellVoltNums= 68
+            self.CellTempNums= 40
+            self.CellFullChrgVolt=4.2
+            self.CellFullChrgCrnt=-self.Capacity/2
+            self.FullChrgSoc=100
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+            self.LookTab_SOC = [0,	     5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100]
+            self.LookTab_OCV = [2.929,	3.2902,	3.3888,	3.425,	3.4846,	3.5382,	3.577,	3.6083,	3.638,	3.6786,	3.7127,	3.7596,	3.7853,	3.8371,	3.9081,	3.9127,	4.0097,	4.0314,	4.0463,	4.0969,	4.174]
+
+            self.CellOvLv1=4.04
+            self.CellOvLv2=4.05
+            self.CellUvLv1=2.99
+            self.CellUvLv2=2.950
+            self.CellVoltDiffLv1=0.35
+            self.CellVoltDiffLv2=0.4
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=10
+            self.CellTempDiffLv2=15
+            self.CellTempRate=5
+
+            self.PackChgOc=-45
+            self.PackDisOc=200
+
+            self.LeakCurrentLv1=-10
+            self.LeakCurrentLv2=-15
+            self.LeakCurrentLv3=-50
+
+            self.TrwVoltRate=-1
+
+        elif celltype==99:   #永联+力神
+            self.Capacity = 40
+            self.CellVoltNums=72
+            self.CellTempNums=32
+            self.CellFullChrgVolt=3.55
+            self.CellFullChrgCrnt=-self.Capacity/2
+
+            self.OcvInflexionBelow=3.281
+            self.OcvInflexion2=3.303
+            self.OcvInflexion3=3.343
+            self.OcvInflexionAbove=3.36
+            self.SocInflexion1=30
+            self.SocInflexion2=60
+            self.SocInflexion3=70
+
+            self.FullChrgSoc=100
+            self.PeakSoc=62.5
+            self.PeakVoltLowLmt=3.35
+            self.PeakVoltUpLmt=3.42
+            self.PeakCellVolt=[3.384,3.385,3.386,3.387,3.388]
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+
+            self.LookTab_SOC = [0,	5,	10,	15,	20,	25,	30,	35,	40,	45,	50,	55,	60,	61,	62,	63,	64,	65,	66,	67,	68,	69,	70,	75,	80,	85,	90,	95,	96,	97,	98,	99,	100]
+            self.LookTab_OCV = [2.6748,	3.2020,	3.2170,	3.2430,	3.2600,	3.2770,	3.2880,	3.2890,	3.2900,	3.2910,	3.2911,	3.2970,	3.3030,	3.3080,	3.3200,	3.3270,	3.3290,	3.3300,	3.3301,	3.3310,	3.3311,	3.3312,	3.3313,	3.3314,	3.3315,	3.3316,	3.3320,	3.3330,	3.3340,	3.3350,	3.3360,	3.3580,	3.3640]
+            self.LookTab_OCVChg=[2.6460, 3.1570, 3.2220, 3.2450,3.2760,	3.2970,	3.3060,	3.3070,	3.3080,	3.3090,	3.3110,	3.3140,	3.3220,	3.3270,	3.3330,	3.3380,	3.3420,	3.3421,	3.3422,	3.3423,	3.3424,	3.3431,	3.3432,	3.3433,	3.3434,	3.3441,	3.3442,	3.3443,	3.3444,	3.3445,	3.3446,	3.3447,	3.3640]
+
+            self.CellOvLv1=3.65
+            self.CellOvLv2=3.7
+            self.CellUvLv1=2.7
+            self.CellUvLv2=2.6
+            self.CellVoltDiffLv1=0.6
+            self.CellVoltDiffLv2=1.1
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=15
+            self.CellTempDiffLv2=20
+            self.CellTempRate=5
+
+            self.PackChgOc=-160
+            self.PackDisOc=180
+
+            self.LeakCurrentLv1=-20
+            self.LeakCurrentLv2=-30
+            self.LeakCurrentLv3=-100
+
+            self.TrwVoltRate=-8  
+            self.mk_slope=-0.8
+        
+
+        else:
+            print('未找到对应电池编号！！！')
+            # sys.exit()

USER/SPF/alibaba/01BatSoh/CBMSBatSoh.py

@@ -2,7 +2,8 @@ import random
 import pandas as pd
 import numpy as np
 import datetime
-from USER.SPF.alibaba.Common import BatParam
+import BatParam
+# from matplotlib import pyplot as plt
 
 class BatSoh():
     def __init__(self,sn,celltype,df_bms,df_soh):  #参数初始化
@@ -15,7 +16,9 @@ class BatSoh():
         self.bms_soc=df_bms['PackSOC']
         self.bms_soh=df_bms['PackSOH']
         self.bmsstat=df_bms['BMSStat']
-        self.bmstime= pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        self.df_bms['time']= pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        self.bmstime=self.df_bms['time']
+
         self.df_soh=df_soh
 
         self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
@@ -99,7 +102,7 @@ class BatSoh():
 
     #获取当前行所有电压数据......................................................................................................................
     def _cellvolt_get(self,num):
-        cellvolt = np.array(self.df_bms.loc[num,self.cellvolt_name])
+        cellvolt = self.df_bms.loc[num,self.cellvolt_name]
         return cellvolt
 
     #获取单个电压值.................................................................................................
@@ -118,19 +121,21 @@ class BatSoh():
         time_last=time_now-datetime.timedelta(seconds=300)
         df_volt=self.df_bms[(self.df_bms['time']>=time_last) & (self.df_bms['time']<=time_now)]
         df_volt=df_volt[self.cellvolt_name]
+        df_volt=df_volt[(df_volt>2) & (df_volt<4.5)]
+        df_volt=df_volt.dropna()
         cellvolt_std=df_volt.std(axis=0)
-        if len(df_volt)>2 and max(cellvolt_std)<1.5:
+        if len(df_volt)>2 and max(cellvolt_std)<0.005:
             cellvolt_sum=df_volt.sum(0)-df_volt.max(0)-df_volt.min(0)
             cellvolt_mean=cellvolt_sum/(len(df_volt)-2)
             cellvolt=cellvolt_mean
         elif len(df_volt)==2:
             # df_volt=pd.DataFrame(df_volt,dtype=np.float)
-            if max(abs(df_volt.iloc[1]-df_volt.iloc[0]))<3:
+            if max(abs(df_volt.iloc[1]-df_volt.iloc[0]))<0.003:
                 cellvolt=df_volt.mean(0)
             else:
                 cellvolt=pd.DataFrame()
         else:
-            cellvolt=pd.DataFrame()
+            cellvolt=df_volt.iloc[0]
         return cellvolt
     
     #dvdq方法计算soh...........................................................................................................................
@@ -209,7 +214,7 @@ class BatSoh():
                 if soc2[PeakIndex]>40 and soc2[PeakIndex]<80:
                     cellsoh_init=(Ah_tatal[-1]-Ah_tatal1[PeakIndex]) * 100 / ((self.param.FullChrgSoc - self.param.PeakSoc) * 0.01 * self.param.Capacity)
                     if cellsoh_init<95:
-                        cellsoh_init=cellsoh_init*0.3926+58.14
+                        cellsoh_init=cellsoh_init*0.3926+58.64
                         return cellsoh_init
                     else:
                         return cellsoh_init
@@ -232,7 +237,7 @@ class BatSoh():
                         xvolt = df_Data1['XVOLT']
                         cellsoh_init=(Ah_tatal[-1]-Ah_tatal1[PeakIndex]) * 100 / ((self.param.FullChrgSoc - self.param.PeakSoc) * 0.01 * self.param.Capacity)
                         if cellsoh_init<95:
-                            cellsoh_init=cellsoh_init*0.3926+58.14
+                            cellsoh_init=cellsoh_init*0.3926+58.64
                             return cellsoh_init
                         else:
                             return cellsoh_init
@@ -314,7 +319,7 @@ class BatSoh():
         standingtime=0
         for i in range(2,len(self.df_bms)-2):
 
-            if abs(self.packcrnt[i]) < 1 and abs(self.packcrnt[i-1]) < 1:     #电流为0
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1:     #电流为0
                 delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
                 standingtime=standingtime+delttime
                 self._celltemp_weight(i)     #获取不同温度对应的静置时间
@@ -324,48 +329,52 @@ class BatSoh():
                         if len(standingpoint_st)>len(standingpoint_sp):     #开始时刻已获取，结束时刻未获取
                             cellvolt_now=self._cellvolt_get(i)     #获取当前行电压数据
                             minocv_socnow=np.interp(min(cellvolt_now),self.param.LookTab_OCV,self.param.LookTab_SOC) 
-                            if 3<max(cellvolt_now)<4.5 and 3<min(cellvolt_now)<4.5:
+                            if 2<max(cellvolt_now)<4.5 and 2<min(cellvolt_now)<4.5:
                                 if (minocv_socst<35 and minocv_socnow>70) or (minocv_socst>70 and minocv_socnow<35):   #当前时刻SOC与开始时刻SOC差>=40
-                                    if abs(self.packcrnt[i+1])>=1:    #如果下一时刻电流>=0.5，则压入当前索引
+                                    if abs(self.packcrnt[i+1])>=0.1:    #如果下一时刻电流>=0.5，则压入当前索引
                                         standingpoint_sp.append(i)
                                         standingpoint_st.append(i)
                                         tempweightlist.append(self.tempweight)
+                                        minocv_socst=minocv_socnow
                                         standingtime=0
                                         continue
                                     else:
-                                        if standingtime>7200 or i==len(self.df_bms)-3:   #仍处于静置，但静置时间>1h，则直接获取sp时刻，或者到了数据末尾
+                                        if standingtime>2*self.StandardStandingTime or i==len(self.df_bms)-3:   #仍处于静置，但静置时间>1h，则直接获取sp时刻，或者到了数据末尾
                                             standingpoint_sp.append(i)
-                                            tempweightlist.append(self.tempweight)
                                             continue
                                 else:
-                                    if abs(self.packcrnt[i+1])>=1:
+                                    if abs(self.packcrnt[i+1])>=0.1:
                                         standingtime=0
-                                        if  minocv_socst<35 and (minocv_socnow<minocv_socst or (self.bmstime[i]-self.bmstime[standingpoint_st[-1]]).total_seconds()>24*3600):
+                                        if  minocv_socnow<35 and (minocv_socnow<minocv_socst or (self.bmstime[i]-self.bmstime[standingpoint_st[-1]]).total_seconds()>24*3600):
                                             standingpoint_st[-1]=i
                                             tempweightlist[-1]=self.tempweight
-                                        elif minocv_socst>=70 and (minocv_socnow>minocv_socst or (self.bmstime[i]-self.bmstime[standingpoint_st[-1]]).total_seconds()>24*3600):
+                                            minocv_socst=minocv_socnow
+                                        elif minocv_socnow>=70 and (minocv_socnow>minocv_socst or (self.bmstime[i]-self.bmstime[standingpoint_st[-1]]).total_seconds()>24*3600):
                                             standingpoint_st[-1]=i
                                             tempweightlist[-1]=self.tempweight
+                                            minocv_socst=minocv_socnow
                                         else:
                                             pass
                                     else:
                                         continue
                         else:
-                            if abs(self.packcrnt[i+1])>=1:
+                            if abs(self.packcrnt[i+1])>=0.1:
                                 cellvolt_now=self._cellvolt_get(i) 
-                                minocv_socst=np.interp(min(cellvolt_now),self.pack_param['discharge_ocv_v'],self.pack_param['discharge_ocv_soc']) 
-                                if 3<max(cellvolt_now)<4.5 and 3<min(cellvolt_now)<4.5 and (minocv_socst<35 or minocv_socst>70):
+                                minocv_socst=np.interp(min(cellvolt_now),self.param.LookTab_OCV,self.param.LookTab_SOC) 
+                                if 2<max(cellvolt_now)<4.5 and 2<min(cellvolt_now)<4.5 and (minocv_socst<35 or minocv_socst>70):
                                     standingpoint_st.append(i)
+                                    tempweightlist.append(self.tempweight)
                                     standingtime=0
                                     continue
                             else:
                                 continue
                     else:
-                        if abs(self.packcrnt[i+1])>=1:
+                        if abs(self.packcrnt[i+1])>=0.1:
                             cellvolt_now=self._cellvolt_get(i)
                             minocv_socst=np.interp(min(cellvolt_now),self.param.LookTab_OCV,self.param.LookTab_SOC) 
-                            if 3<max(cellvolt_now)<4.5 and 3<min(cellvolt_now)<4.5 and (minocv_socst<35 or minocv_socst>70):
+                            if 2<max(cellvolt_now)<4.5 and 2<min(cellvolt_now)<4.5 and (minocv_socst<35 or minocv_socst>70):
                                 standingpoint_st.append(i)
+                                tempweightlist.append(self.tempweight)
                                 standingtime=0
                                 continue
                         else:
@@ -382,6 +391,7 @@ class BatSoh():
             df_res=pd.DataFrame(columns=column_name)
 
             for i in range(len(standingpoint_sp)):
+                continue_flg=0
                 cellocv_st=self._avgvolt_get(standingpoint_st[i])    #获取静置点所有电芯的电压
                 cellocv_sp=self._avgvolt_get(standingpoint_sp[i])
                 # accumtime=self.accumtime.to_list()  #累计量的时间列表
@@ -401,119 +411,127 @@ class BatSoh():
                             ah_packcrnt_dis=ah_packcrnt_dis+self.packcrnt[j+1]*Step
                         else:
                             ah_packcrnt_chg=ah_packcrnt_chg-self.packcrnt[j+1]*Step
-                ah_packcrnt_chg=ah_packcrnt_chg/3600
-                ah_packcrnt_dis=ah_packcrnt_dis/3600
-                ah_packcrnt=ah_packcrnt_chg-ah_packcrnt_dis     #两个静置点的总累计AH，负值代表放电，正值代表充电
-
-                # ah_accum_dis=self.df_accum.loc[timepoint_accum_sp,'累计放电电量']-self.df_accum.loc[timepoint_accum_st,'累计放电电量']  #两个静置点之间的放电电量
-                # ah_accum_chg=self.df_accum.loc[timepoint_accum_sp,'累计充电电量']-self.df_accum.loc[timepoint_accum_st,'累计充电电量']  #两个静置点之间的充电电量
-                # ah_accum_tatol=ah_accum_chg-ah_accum_dis  #两个静置点的总累计AH，负值代表放电，正值代表充电
-                ah_accum=ah_packcrnt
-
-                delt_days=(self.bmstime[standingpoint_sp[i]]-self.bmstime[standingpoint_st[i]]).total_seconds()/(3600*24)
-                if delt_days<=1: #两次时间间隔对计算结果的影响
-                    soh_weight1=1
-                elif delt_days<=2:
-                    soh_weight1=0.7
-                elif delt_days<=3:
-                    soh_weight1=0.4
-                else:
-                    soh_weight1=0
-
-                # if ah_packcrnt_dis<self.param.Capacity: #放电ah数对结果的影响
-                #     soh_weight1=(1-ah_packcrnt_dis/(self.param.Capacity*1.5))*soh_weight1
-                # else:
-                #     soh_weight1=0.1
-
-                # if self.param.Capacity**0.7*0.4 < abs(ah_accum_tatol) < self.param.Capacity:    #累计量的权重
-                #     if abs(ah_accum_tatol-ah_packcrnt)<self.param.Capacity/20:
-                #         soh_weight1=soh_weight1*1
-                #     elif abs(ah_accum_tatol-ah_packcrnt) < self.param.Capacity/10:
-                #         soh_weight1=soh_weight1*0.8
-                #     else:
-                #         soh_weight1=soh_weight1*0.5
-                # else:
-                #     if self.param.Capacity*0.7*0.4< abs(ah_packcrnt) <self.param.Capacity:
-                #         soh_weight1=soh_weight1*0.3
-                #     else:
-                #         soh_weight1=0
-
-                #计算每个电芯的SOH值
-                cellsoh=[]
-                if (not cellocv_st.empty) and (not cellocv_sp.empty):
-                    for j in range(self.param.CellVoltNums):
-                        ocv_soc1=np.interp(cellocv_st[j],self.param.LookTab_OCV,self.param.LookTab_SOC)
-                        ocv_soc2=np.interp(cellocv_sp[j],self.param.LookTab_OCV,self.param.LookTab_SOC)
+                    elif 120<=Step<600 and self.packcrnt[j+1]<-5 and abs(self.packcrnt[j+1]-self.packcrnt[j])<3:
+                        ah_packcrnt_chg=ah_packcrnt_chg-self.packcrnt[j+1]*Step
+                    elif (abs(self.packcrnt[j+1]*Step/3600)>1 and abs(self.packcrnt[j+1])>1) or (abs(self.packcrnt[j]*Step/3600)>1 and abs(self.packcrnt[j])>5):
+                        if abs(self.bmssoc[j+1]-self.bmssoc[j])>2:
+                            continue_flg=1
+                    else:
+                        pass 
+                if continue_flg==0:
+                    ah_packcrnt_chg=ah_packcrnt_chg/3600
+                    ah_packcrnt_dis=ah_packcrnt_dis/3600
+                    ah_packcrnt=ah_packcrnt_chg-ah_packcrnt_dis     #两个静置点的总累计AH，负值代表放电，正值代表充电
+
+                    # ah_accum_dis=self.df_accum.loc[timepoint_accum_sp,'累计放电电量']-self.df_accum.loc[timepoint_accum_st,'累计放电电量']  #两个静置点之间的放电电量
+                    # ah_accum_chg=self.df_accum.loc[timepoint_accum_sp,'累计充电电量']-self.df_accum.loc[timepoint_accum_st,'累计充电电量']  #两个静置点之间的充电电量
+                    # ah_accum_tatol=ah_accum_chg-ah_accum_dis  #两个静置点的总累计AH，负值代表放电，正值代表充电
+                    ah_accum=ah_packcrnt
+
+                    delt_days=(self.bmstime[standingpoint_sp[i]]-self.bmstime[standingpoint_st[i]]).total_seconds()/(3600*24)
+                    if delt_days<=1: #两次时间间隔对计算结果的影响
+                        soh_weight1=1
+                    elif delt_days<=2:
+                        soh_weight1=0.7
+                    elif delt_days<=3:
+                        soh_weight1=0.4
+                    else:
+                        soh_weight1=0
+
+                    # if ah_packcrnt_dis<self.param.Capacity: #放电ah数对结果的影响
+                    #     soh_weight1=(1-ah_packcrnt_dis/(self.param.Capacity*1.5))*soh_weight1
+                    # else:
+                    #     soh_weight1=0.1
+
+                    # if self.param.Capacity**0.7*0.4 < abs(ah_accum_tatol) < self.param.Capacity:    #累计量的权重
+                    #     if abs(ah_accum_tatol-ah_packcrnt)<self.param.Capacity/20:
+                    #         soh_weight1=soh_weight1*1
+                    #     elif abs(ah_accum_tatol-ah_packcrnt) < self.param.Capacity/10:
+                    #         soh_weight1=soh_weight1*0.8
+                    #     else:
+                    #         soh_weight1=soh_weight1*0.5
+                    # else:
+                    #     if self.param.Capacity*0.7*0.4< abs(ah_packcrnt) <self.param.Capacity:
+                    #         soh_weight1=soh_weight1*0.3
+                    #     else:
+                    #         soh_weight1=0
+
+                    #计算每个电芯的SOH值
+                    cellsoh=[]
+                    if (not cellocv_st.empty) and (not cellocv_sp.empty):
+                        for j in range(self.param.CellVoltNums):
+                            ocv_soc1=np.interp(cellocv_st[j],self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            ocv_soc2=np.interp(cellocv_sp[j],self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            delt_ocv_soc=ocv_soc2-ocv_soc1
+                            delt_ocv_soc_weight=self._deltsoc_weight(abs(delt_ocv_soc))
+                            soh_weight=soh_weight1*tempweightlist[i]*delt_ocv_soc_weight*0.5
+                            cellsoh_init=ah_accum*100/((ocv_soc2-ocv_soc1)*0.01*self.param.Capacity)
+
+                            if cellsoh_init>60 and cellsoh_init<120 and soh_weight>0.1:    #判断soh值的有效区间
+                                if len(df_res)<1:
+                                    if not self.df_soh.empty:
+                                        cellsoh_last=eval(self.df_soh.loc[len(self.df_soh)-1,'cellsoh'])
+                                        # if soh_weight>1/abs(cellsoh_init-cellsoh_last[j]):
+                                        #     soh_weight=1/abs(cellsoh_init-cellsoh_last[j])
+                                        #     cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                                        # else:
+                                        cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                                    else:
+                                        cellsoh_cal=cellsoh_init
+                                else:
+                                    cellsoh_last=eval(df_res.loc[len(df_res)-1,'cellsoh'])
+                                    # if soh_weight>1/abs(cellsoh_init-cellsoh_last[j]):
+                                    #     soh_weight=1/abs(cellsoh_init-cellsoh_last[j])
+                                    #     cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                                    # else:
+                                    cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                                cellsoh_cal=eval(format(cellsoh_cal,'.1f'))
+                                cellsoh.append(cellsoh_cal)
+                            else:
+                                cellsoh=[]
+                                break
+                        #计算电池包SOH
+                        ocv_soc1=np.interp(min(cellocv_st),self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        ocv_soc2=np.interp(max(cellocv_sp),self.param.LookTab_OCV,self.param.LookTab_SOC)
                         delt_ocv_soc=ocv_soc2-ocv_soc1
                         delt_ocv_soc_weight=self._deltsoc_weight(abs(delt_ocv_soc))
                         soh_weight=soh_weight1*tempweightlist[i]*delt_ocv_soc_weight*0.5
-                        cellsoh_init=ah_accum*100/((ocv_soc2-ocv_soc1)*0.01*self.param.Capacity)
+                        packsoh_init=ah_accum*100/((ocv_soc2-ocv_soc1)*0.01*self.param.Capacity)
 
-                        if cellsoh_init>60 and cellsoh_init<120 and soh_weight>0.1:    #判断soh值的有效区间
+                        if packsoh_init>55 and packsoh_init<120 and soh_weight>0.1:    #判断soh值的有效区间
                             if len(df_res)<1:
                                 if not self.df_soh.empty:
-                                    cellsoh_last=eval(self.df_soh.loc[len(self.df_soh)-1,'cellsoh'])
-                                    # if soh_weight>1/abs(cellsoh_init-cellsoh_last[j]):
-                                    #     soh_weight=1/abs(cellsoh_init-cellsoh_last[j])
-                                    #     cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                                    packsoh_last=self.df_soh.loc[len(self.df_soh)-1,'packsoh']
+                                    # if soh_weight>1/abs(packsoh_init-packsoh_last):
+                                    #     soh_weight=1/abs(packsoh_init-packsoh_last)
+                                    #     packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
                                     # else:
-                                    cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
+                                    packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
                                 else:
-                                    cellsoh_cal=cellsoh_init
+                                    packsoh=packsoh_init
                             else:
-                                cellsoh_last=eval(df_res.loc[len(df_res)-1,'cellsoh'])
-                                # if soh_weight>1/abs(cellsoh_init-cellsoh_last[j]):
-                                #     soh_weight=1/abs(cellsoh_init-cellsoh_last[j])
-                                #     cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
-                                # else:
-                                cellsoh_cal=cellsoh_init*soh_weight + cellsoh_last[j]*(1-soh_weight)
-                            cellsoh_cal=eval(format(cellsoh_cal,'.1f'))
-                            cellsoh.append(cellsoh_cal)
-                        else:
-                            cellsoh=[]
-                            break
-                    #计算电池包SOH
-                    ocv_soc1=np.interp(min(cellocv_st),self.param.LookTab_OCV,self.param.LookTab_SOC)
-                    ocv_soc2=np.interp(max(cellocv_sp),self.param.LookTab_OCV,self.param.LookTab_SOC)
-                    delt_ocv_soc=ocv_soc2-ocv_soc1
-                    delt_ocv_soc_weight=self._deltsoc_weight(abs(delt_ocv_soc))
-                    soh_weight=soh_weight1*tempweightlist[i]*delt_ocv_soc_weight*0.5
-                    packsoh_init=ah_accum*100/((ocv_soc2-ocv_soc1)*0.01*self.param.Capacity)
-
-                    if packsoh_init>55 and packsoh_init<120 and soh_weight>0.1:    #判断soh值的有效区间
-                        if len(df_res)<1:
-                            if not self.df_soh.empty:
-                                packsoh_last=self.df_soh.loc[len(self.df_soh)-1,'packsoh']
+                                packsoh_last=df_res.loc[len(df_res)-1,'packsoh']
                                 # if soh_weight>1/abs(packsoh_init-packsoh_last):
-                                #     soh_weight=1/abs(packsoh_init-packsoh_last)
-                                #     packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
+                                #         soh_weight=1/abs(packsoh_init-packsoh_last)
+                                #         packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
                                 # else:
                                 packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
-                            else:
-                                packsoh=packsoh_init
+                            packsoh=eval(format(packsoh,'.1f'))
                         else:
-                            packsoh_last=df_res.loc[len(df_res)-1,'packsoh']
-                            # if soh_weight>1/abs(packsoh_init-packsoh_last):
-                            #         soh_weight=1/abs(packsoh_init-packsoh_last)
-                            #         packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
-                            # else:
-                            packsoh=packsoh_init*soh_weight + packsoh_last*(1-soh_weight)
-                        packsoh=eval(format(packsoh,'.1f'))
-                    else:
-                        packsoh=0
-                        continue
+                            packsoh=0
+                            continue
 
-                    if cellsoh and 55<min(cellsoh)<120 and 50<packsoh<120:
-                        soh=min(cellsoh)
-                        cellsohmin=soh
-                        cellsohmax=max(cellsoh)
-                        sohmin_num=cellsoh.index(cellsohmin)+1
-                        sohmax_num=cellsoh.index(cellsohmax)+1
-                        cellsoh_diff=cellsohmax-cellsohmin
-                        bmssoh=self.bms_soh[standingpoint_sp[i]]
-                        df_res.loc[len(df_res)]=[timepoint_bms_st, timepoint_bms_sp, self.sn, 1, bmssoh, packsoh, soh, cellsohmin, cellsohmax, sohmin_num, sohmax_num, cellsoh_diff, str(cellsoh)]
-                    else:
-                        continue
+                        if cellsoh and 55<min(cellsoh)<120 and 50<packsoh<120:
+                            soh=min(cellsoh)
+                            cellsohmin=soh
+                            cellsohmax=max(cellsoh)
+                            sohmin_num=cellsoh.index(cellsohmin)+1
+                            sohmax_num=cellsoh.index(cellsohmax)+1
+                            cellsoh_diff=cellsohmax-cellsohmin
+                            bmssoh=self.bms_soh[standingpoint_sp[i]]
+                            df_res.loc[len(df_res)]=[timepoint_bms_st, timepoint_bms_sp, self.sn, 1, bmssoh, packsoh, soh, cellsohmin, cellsohmax, sohmin_num, sohmax_num, cellsoh_diff, str(cellsoh)]
+                        else:
+                            continue
             if df_res.empty:
                 return pd.DataFrame()
             else:
@@ -536,34 +554,43 @@ class BatSoh():
         for i in range(3,len(self.df_bms)-3):
 
             #获取两点法法所需数据-开始.................................................................................................................
-            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     #判断非平台区静置状态
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1:     #判断非平台区静置状态
                 delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
                 standingtime=standingtime+delttime
                 self._celltemp_weight(i)     #获取不同温度对应的静置时间
 
                 if standingtime>self.StandardStandingTime:  #静置时间满足要求
-                    if abs(self.packcrnt[i+2])>=0.1:  #下一时刻电流>0.1A
-                        standingtime=0
-                        cellvolt_now=self._cellvolt_get(i)
-                        cellvolt_now=cellvolt_now[(cellvolt_now>2) & (cellvolt_now<3.8)]
-                        if (not cellvolt_now.empty) and 2<max(cellvolt_now)<self.param.OcvInflexionBelow-0.001:      #当前最大电芯电压<OCV下拐点
-                            if standingpoint_st:
-                                if len(standingpoint_st)>len(standingpoint_sp):
-                                    if self.packcrnt[standingpoint_st[-1]]<-1:     #判断上一次静置点的是否为满充
+                    cellvolt_now=self._cellvolt_get(i)
+                    cellvolt_now=cellvolt_now[(cellvolt_now>2) & (cellvolt_now<3.8)]
+                    if (not cellvolt_now.empty) and 2<max(cellvolt_now)<self.param.OcvInflexionBelow-0.001:      #当前最大电芯电压<OCV下拐点
+                        if standingpoint_st:
+                            if len(standingpoint_st)>len(standingpoint_sp):
+                                if self.packcrnt[standingpoint_st[-1]]<=-1:     #判断上一次静置点的为满充
+                                    if abs(self.packcrnt[i+1])>=1:  #下一时刻电流>0.1A
+                                        standingtime=0
                                         standingpoint_sp.append(i)
                                         standingpoint_st.append(i)
                                         tempweightlist1.append(self.tempweight)
                                     else:
+                                        if standingtime>2*self.StandardStandingTime or i==len(self.df_bms)-3:
+                                            standingpoint_sp.append(i)
+                                            standingpoint_st.append(i)
+                                            tempweightlist1.append(self.tempweight)
+                                else:
+                                    if abs(self.packcrnt[i+1])>=1:  #下一时刻电流>0.1A
+                                        standingtime=0
                                         standingpoint_st[-1]=i
                                         tempweightlist1[-1]=self.tempweight
-                                else:
+                            else:
+                                if abs(self.packcrnt[i+1])>=1:  #下一时刻电流>0.1A
+                                    standingtime=0
                                     standingpoint_st.append(i)
                                     tempweightlist1.append(self.tempweight)
-                            else:
+                        else:
+                            if abs(self.packcrnt[i+1])>=1:  #下一时刻电流>0.1A
+                                standingtime=0
                                 standingpoint_st.append(i)
                                 tempweightlist1.append(self.tempweight)
-                        else:
-                            pass
                     else:
                         pass
                 else:
@@ -572,9 +599,10 @@ class BatSoh():
             elif self.packcrnt[i]<=-1 and self.packcrnt[i-1]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i+2]>-1:    #判读满充状态
                 standingtime=0
                 self._celltemp_weight(i)
-                cellvolt_now=self._cellvolt_get(i).tolist()
+                cellvolt_now=self._cellvolt_get(i)
                 cellvolt_now=cellvolt_now[(cellvolt_now>2) & (cellvolt_now<3.8)]
                 if (not cellvolt_now.empty) and max(cellvolt_now)>self.param.CellFullChrgVolt:
+                    cellvolt_now=cellvolt_now.tolist()
                     if standingpoint_st:
                         if len(standingpoint_st)>len(standingpoint_sp):
                             if abs(self.packcrnt[standingpoint_st[-1]])<0.5:     #判断上一次静置点是否为下拐点
@@ -619,7 +647,7 @@ class BatSoh():
                     charging=0
                     continue
                 elif self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and  self.packcrnt[i+2]>-1:  #判断电流波动时刻
-                    cellvolt_now=self._cellvolt_get(i+1)
+                    cellvolt_now=self._cellvolt_get(i+1).tolist()
                     if max(cellvolt_now)>self.param.CellFullChrgVolt:   #电压>满充电压
                         chrg_end.append(i+1)
                         cellmaxvolt_number2.append(cellvolt_now.index(max(cellvolt_now)))   #获取最大电压索引
@@ -659,20 +687,28 @@ class BatSoh():
             #两点法计算SOH........................................................................................................................................
             if standingpoint_sp:
                 for i in range(len(standingpoint_sp)):  #判断为满充点或者下拐点
+                    continue_flg=0
                     if self.packcrnt[standingpoint_sp[i]]<=-1:  #计算单体电芯soh
-                        cellocv_st=self._cellvolt_get(standingpoint_st[i])
-                        ocv_soc1=np.interp(cellocv_st[cellmaxvolt_number1[i]],self.param.LookTab_OCV,self.param.LookTab_SOC)
-                        ocv_packsoc1=np.interp(min(cellocv_st),self.param.LookTab_OCV,self.param.LookTab_SOC)
-                        ocv_soc2=self.param.FullChrgSoc
-                        ocv_packsoc2=ocv_soc2
+                        cellocv_st=self._avgvolt_get(standingpoint_st[i])
+                        if not cellocv_st.empty:
+                            ocv_soc1=np.interp(cellocv_st[cellmaxvolt_number1[i]],self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            ocv_packsoc1=np.interp(min(cellocv_st),self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            ocv_soc2=self.param.FullChrgSoc
+                            ocv_packsoc2=ocv_soc2
+                        else:
+                            ocv_soc1=100
+                            ocv_soc2=99
                     else:
-                        cellocv_sp=self._cellvolt_get(standingpoint_sp[i])
-                        ocv_soc1=self.param.FullChrgSoc
-                        ocv_packsoc1=ocv_soc1
-                        ocv_soc2=np.interp(cellocv_sp[cellmaxvolt_number1[i]],self.param.LookTab_OCV,self.param.LookTab_SOC)
-                        ocv_packsoc2=np.interp(min(cellocv_sp),self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        cellocv_sp=self._avgvolt_get(standingpoint_sp[i])
+                        if not cellocv_sp.empty:
+                            ocv_soc1=self.param.FullChrgSoc
+                            ocv_packsoc1=ocv_soc1
+                            ocv_soc2=np.interp(cellocv_sp[cellmaxvolt_number1[i]],self.param.LookTab_OCV,self.param.LookTab_SOC)
+                            ocv_packsoc2=np.interp(min(cellocv_sp),self.param.LookTab_OCV,self.param.LookTab_SOC)
+                        else:
+                            ocv_soc1=100
+                            ocv_soc2=99
 
-                    cellocv_sp=self._cellvolt_get(standingpoint_sp[i])
                     # accumtime=self.accumtime.to_list()  #累计量的时间列表
                     timepoint_bms_st=self.bmstime[standingpoint_st[i]]   #获取静置点的时间
                     timepoint_bms_sp=self.bmstime[standingpoint_sp[i]]
@@ -690,50 +726,57 @@ class BatSoh():
                                 ah_packcrnt_dis=ah_packcrnt_dis+self.packcrnt[j+1]*Step
                             else:
                                 ah_packcrnt_chg=ah_packcrnt_chg-self.packcrnt[j+1]*Step
-                    ah_packcrnt_chg=ah_packcrnt_chg/3600
-                    ah_packcrnt_dis=ah_packcrnt_dis/3600
-                    ah_packcrnt=ah_packcrnt_chg-ah_packcrnt_dis     #两个静置点的总累计AH，负值代表放电，正值代表充电
-
-                    # ah_accum_dis=self.df_accum.loc[timepoint_accum_sp,'累计放电电量']-self.df_accum.loc[timepoint_accum_st,'累计放电电量']  #两个静置点之间的放电电量
-                    # ah_accum_chg=self.df_accum.loc[timepoint_accum_sp,'累计充电电量']-self.df_accum.loc[timepoint_accum_st,'累计充电电量']  #两个静置点之间的充电电量
-                    # ah_accum_tatol=ah_accum_chg-ah_accum_dis  #两个静置点的总累计AH，负值代表放电，正值代表充电
-                    ah_accum=ah_packcrnt
+                        elif 120<=Step<1000 and self.packcrnt[j+1]<-5 and abs(self.packcrnt[j+1]-self.packcrnt[j])<3:
+                            ah_packcrnt_chg=ah_packcrnt_chg-self.packcrnt[j+1]*Step
+                        elif (abs(self.packcrnt[j+1]*Step/3600)>1 and abs(self.packcrnt[j+1])>1) or (abs(self.packcrnt[j]*Step/3600)>1 and abs(self.packcrnt[j])>5):
+                            continue_flg=1
+                        else:
+                            pass 
+                    if continue_flg==0:
+                        ah_packcrnt_chg=ah_packcrnt_chg/3600
+                        ah_packcrnt_dis=ah_packcrnt_dis/3600
+                        ah_packcrnt=ah_packcrnt_chg-ah_packcrnt_dis     #两个静置点的总累计AH，负值代表放电，正值代表充电
+
+                        # ah_accum_dis=self.df_accum.loc[timepoint_accum_sp,'累计放电电量']-self.df_accum.loc[timepoint_accum_st,'累计放电电量']  #两个静置点之间的放电电量
+                        # ah_accum_chg=self.df_accum.loc[timepoint_accum_sp,'累计充电电量']-self.df_accum.loc[timepoint_accum_st,'累计充电电量']  #两个静置点之间的充电电量
+                        # ah_accum_tatol=ah_accum_chg-ah_accum_dis  #两个静置点的总累计AH，负值代表放电，正值代表充电
+                        ah_accum=ah_packcrnt
+
+                        delt_days=(self.bmstime[standingpoint_sp[i]]-self.bmstime[standingpoint_st[i]]).total_seconds()/(3600*24)
+                        if delt_days<=1: #两次时间间隔对计算结果的影响
+                            soh_weight=1
+                        elif delt_days<=2:
+                            soh_weight=0.7
+                        elif delt_days<=3:
+                            soh_weight=0.4
+                        else:
+                            soh_weight=0
+
+                        # if self.param.Capacity*0.65*0.7 < abs(ah_packcrnt) < self.param.Capacity:    #累计量的权重
+                        #     if abs(ah_accum_tatol-ah_packcrnt)<self.param.Capacity/20:
+                        #         soh_weight=soh_weight*1
+                        #     elif abs(ah_accum_tatol-ah_packcrnt)<self.param.Capacity/10:
+                        #         soh_weight=soh_weight*0.8
+                        #     else:
+                        #         soh_weight=soh_weight*0.5
+                        # else:
+                        #     if self.param.Capacity*0.65*0.7 < abs(ah_accum) < self.param.Capacity:
+                        #         soh_weight=soh_weight*0.3
+                        #     else:
+                        #         soh_weight=0
 
-                    delt_days=(self.bmstime[standingpoint_sp[i]]-self.bmstime[standingpoint_st[i]]).total_seconds()/(3600*24)
-                    if delt_days<=1: #两次时间间隔对计算结果的影响
-                        soh_weight=1
-                    elif delt_days<=2:
-                        soh_weight=0.7
-                    elif delt_days<=3:
-                        soh_weight=0.4
-                    else:
-                        soh_weight=0
+                        delt_ocv_soc=ocv_soc2-ocv_soc1
+                        delt_ocv_packsoc=ocv_packsoc2-ocv_packsoc1
+                        delt_ocv_soc_weight=self._deltsoc_weight(abs(delt_ocv_soc))
+                        soh_weight=soh_weight*tempweightlist1[i]*delt_ocv_soc_weight*0.5
+                        cellsoh_init=ah_accum*100/(delt_ocv_soc*0.01*self.param.Capacity)
+                        packsoh_init=ah_accum*100/(delt_ocv_packsoc*0.01*self.param.Capacity)
+                        bmssoh=self.bms_soh[standingpoint_sp[i]]
 
-                    # if self.param.Capacity*0.65*0.7 < abs(ah_packcrnt) < self.param.Capacity:    #累计量的权重
-                    #     if abs(ah_accum_tatol-ah_packcrnt)<self.param.Capacity/20:
-                    #         soh_weight=soh_weight*1
-                    #     elif abs(ah_accum_tatol-ah_packcrnt)<self.param.Capacity/10:
-                    #         soh_weight=soh_weight*0.8
-                    #     else:
-                    #         soh_weight=soh_weight*0.5
-                    # else:
-                    #     if self.param.Capacity*0.65*0.7 < abs(ah_accum) < self.param.Capacity:
-                    #         soh_weight=soh_weight*0.3
-                    #     else:
-                    #         soh_weight=0
-
-                    delt_ocv_soc=ocv_soc2-ocv_soc1
-                    delt_ocv_packsoc=ocv_packsoc2-ocv_packsoc1
-                    delt_ocv_soc_weight=self._deltsoc_weight(abs(delt_ocv_soc))
-                    soh_weight=soh_weight*tempweightlist1[i]*delt_ocv_soc_weight*0.5
-                    cellsoh_init=ah_accum*100/(delt_ocv_soc*0.01*self.param.Capacity)
-                    packsoh_init=ah_accum*100/(delt_ocv_packsoc*0.01*self.param.Capacity)
-                    bmssoh=self.bms_soh[standingpoint_sp[i]]
-
-                    if 55<cellsoh_init<120 and 50<packsoh_init<120:    #判断soh值的有效区间
-                        df_res.loc[len(df_res)]=[timepoint_bms_st, timepoint_bms_sp, self.sn, 1, bmssoh, packsoh_init, cellsoh_init,0,0,0,0,0,0, soh_weight]
-                    else:
-                        pass
+                        if 55<cellsoh_init<120 and 50<packsoh_init<120:    #判断soh值的有效区间
+                            df_res.loc[len(df_res)]=[timepoint_bms_st, timepoint_bms_sp, self.sn, 1, bmssoh, packsoh_init, cellsoh_init,0,0,0,0,0,0, soh_weight]
+                        else:
+                            pass
             else:
                 pass
 
@@ -786,7 +829,7 @@ class BatSoh():
                         if 55<cellsoh_cal<120:
                             cellsoh=[cellsoh_cal+random.uniform(-1.5,1.5) for i in range(self.param.CellVoltNums)]
                             cellsoh=list(map(lambda x:eval(format(x,'.1f')), cellsoh))
-                            df_res.loc[i,'soh']=min(cellsoh)
+                            df_res.loc[i,'soh']=cellsoh_cal
                             df_res.loc[i,'packsoh']=packsoh
                             df_res.loc[i,'cellsoh']=str(cellsoh)
                             df_res.loc[i,'cellsohmin']=min(cellsoh)
@@ -811,7 +854,7 @@ class BatSoh():
                         if 55<cellsoh_cal<120:
                             cellsoh=[cellsoh_cal+random.uniform(-1.5,1.5) for i in range(self.param.CellVoltNums)]
                             cellsoh=list(map(lambda x:eval(format(x,'.1f')), cellsoh))
-                            df_res.loc[i,'soh']=min(cellsoh)
+                            df_res.loc[i,'soh']=cellsoh_cal
                             df_res.loc[i,'packsoh']=packsoh
                             df_res.loc[i,'cellsoh']=str(cellsoh)
                             df_res.loc[i,'cellsohmin']=min(cellsoh)

USER/SPF/alibaba/01BatSoh/main.py

@@ -1,12 +1,13 @@
 import datetime
 import pandas as pd
+import numpy as np
 import datetime
 from USER.SPF.alibaba.Common import DBDownload
 from USER.SPF.alibaba.Common import log
 import CBMSBatSoh
 
 if __name__ == "__main__":
-    SNnums= ['LISHEN01']
+    SNnums= ['LS002']
 
     end_time=datetime.datetime.now()-datetime.timedelta(seconds=10)
     start_time=end_time-datetime.timedelta(seconds=12000)
@@ -36,26 +37,37 @@ if __name__ == "__main__":
             celltype=99
 
             #............................................................读取原始数据库数据...........................................................................
-            df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\力神40Ah-SOH原始数据.xlsx')
+            df_bms= pd.read_excel(r'D:\01WorkSpace\项目\Alibaba\data\原始数据_2022-01-10_2022-02-10\力神_LS002.xlsx')
+            cellvolt_name=['CellVolt'+str(x) for x in range(1,73)]
+            celltemp_name=['CellTemp'+str(x) for x in range(1,33)]
+            df_volt=pd.DataFrame([x[0].split(",") for x in np.array(df_bms[['cell_volt']])])
+            df_volt.columns = cellvolt_name
+            df_volt=df_volt.astype('float')
+            df_temp=pd.DataFrame([x[0].split(",") for x in np.array(df_bms[['cell_temp']])])
+            df_temp.columns = celltemp_name
+            df_temp=df_temp.astype('float')
+            df_bms=pd.concat([df_bms,df_volt,df_temp],axis=1)
+            df_bms.dropna(axis=0, inplace=True)
+            df_bms=df_bms.reset_index(drop=True)
 
             #............................................................读取结果数据库数据.........................................................................
-            host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
-            port=3306
-            db='qx_cas'
-            user='qx_read'
-            password='Qx@123456'
-            mode=1
-            tablename='cellstateestimation_soh'
-            DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
-            with DBRead as DBRead:
-                df_soh=DBRead.getdata(param='time_st,time_sp,sn,method,soh,cellsoh,packsoh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+            # host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+            # port=3306
+            # db='qx_cas'
+            # user='qx_read'
+            # password='Qx@123456'
+            # mode=1
+            # tablename='cellstateestimation_soh'
+            # DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
+            # with DBRead as DBRead:
+            #     df_soh=DBRead.getdata(param='time_st,time_sp,sn,method,soh,cellsoh,packsoh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
             df_soh=pd.DataFrame()
             # print(df_soh)
 
             #.................................................................计算soh...................................................................................
             BatSoh=CBMSBatSoh.BatSoh(sn,celltype,df_bms,df_soh)
             df_res=BatSoh.batsoh()
-            df_res.to_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\99Result\\'+'CBMS_SOH_'+sn+'.xlsx')
+            df_res.to_excel(r'D:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\99Result\\'+'CBMS_SOH_'+sn+'.xlsx')
             # print('done！！！')
              
         except Exception as e:

USER/SPF/alibaba/04BatSoc/BatParam.py

@@ -0,0 +1,196 @@
+
+#定义电池参数
+
+class BatParam:
+    def __init__(self,celltype):
+
+        #公用参数................................................................................................................................................
+        #热失控参数
+        self.TrwTempHigh=60
+        self.TrwTempRate=3
+        self.TrwTempDiff=15
+        self.TrwCellVoltDiff=2.5
+        self.TrwCellVoltFall=0.5
+        self.TrwCellVoltLow=1.5
+        self.TrwPackVoltFall=1.5
+        
+        #故障诊断参数
+        self.temp_time=10
+        self.volt_time=10
+
+        self.SocJump=10
+        self.SocClamp=0.1
+        self.SocLow=3
+        self.SocDiff=20
+
+        self.SohLow=65
+        self.SohDiff=15
+
+        #mana-kendall趋势检验参数
+        self.mk_p=0.001
+        self.mk_z=-5
+        self.mk_Tau=-0.7
+        self.mk_slope=-0.1
+        self.mk_s=-260
+        self.mk_svar=1600
+
+        self.OcvWeight_Temp=[-30,-20,-10,0,10,20,30,40,50]
+        self.OcvWeight_StandingTime=[0,500,600,1200,1800,3600,7200,10800]
+        self.OcvWeight            =[[0,0,  0,  0,    0,   0.1,0.3, 1],
+                                    [0,0,  0,  0,    0,   0.1,0.3, 1],
+                                    [0,0,  0,  0,    0,   0.2,0.5, 1],
+                                    [0,0,  0,  0,    0.2, 0.4,0.8, 1],
+                                    [0,0,  0,  0.1,  0.3, 0.6,1,   1],
+                                    [0,0,  0.1,0.2,  0.5, 0.8,1,   1],
+                                    [0,0,  0.1,0.3,  0.6, 1,  1,   1],
+                                    [0,0,  0.1,0.3,  0.7, 1,  1,   1],
+                                    [0,0,  0.2,0.3,  0.8, 1,  1,   1]]
+
+
+        if celltype==1: #永联+康普顿21700
+            self.Capacity = 4*2
+            self.PackFullChrgVolt=69.99
+            self.CellFullChrgVolt=4.2
+            self.CellFullChrgCrnt=-4
+            self.CellVoltNums=120
+            self.CellTempNums=40
+            self.FullChrgSoc=90
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+            self.LookTab_SOC = [0,	     5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100]
+            self.LookTab_OCV = [2.8497,	3.0924,	3.2868,	3.4117,	3.4461,	3.5032,	3.5602,	3.6049,	3.6368,	3.6709,	3.7121,	3.7593,	3.8237,	3.8755,	3.9093,	3.9518,	4.0119,	4.0615,	4.077,	4.0969,	4.1821]
+
+            self.CellOvLv1=4.2
+            self.CellOvLv2=4.25
+            self.CellUvLv1=2.8
+            self.CellUvLv2=2.5
+            self.CellVoltDiffLv1=0.3
+            self.CellVoltDiffLv2=0.5
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=10
+            self.CellTempDiffLv2=15
+            self.CellTempRate=5
+
+            self.PackChgOc=-40
+            self.PackDisOc=200
+
+            self.LeakCurrentLv1=-10
+            self.LeakCurrentLv2=-15
+            self.LeakCurrentLv3=-50
+
+            self.TrwVoltRate=-1
+
+        elif celltype==2: #中恒+华庭18650
+            self.Capacity = 2*9
+            self.CellVoltNums= 68
+            self.CellTempNums= 40
+            self.CellFullChrgVolt=4.2
+            self.CellFullChrgCrnt=-self.Capacity/2
+            self.FullChrgSoc=100
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+            self.LookTab_SOC = [0,	     5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100]
+            self.LookTab_OCV = [2.929,	3.2902,	3.3888,	3.425,	3.4846,	3.5382,	3.577,	3.6083,	3.638,	3.6786,	3.7127,	3.7596,	3.7853,	3.8371,	3.9081,	3.9127,	4.0097,	4.0314,	4.0463,	4.0969,	4.174]
+
+            self.CellOvLv1=4.04
+            self.CellOvLv2=4.05
+            self.CellUvLv1=2.99
+            self.CellUvLv2=2.950
+            self.CellVoltDiffLv1=0.35
+            self.CellVoltDiffLv2=0.4
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=10
+            self.CellTempDiffLv2=15
+            self.CellTempRate=5
+
+            self.PackChgOc=-45
+            self.PackDisOc=200
+
+            self.LeakCurrentLv1=-10
+            self.LeakCurrentLv2=-15
+            self.LeakCurrentLv3=-50
+
+            self.TrwVoltRate=-1
+
+        elif celltype==99:   #永联+力神
+            self.Capacity = 40
+            self.CellVoltNums=72
+            self.CellTempNums=32
+            self.CellFullChrgVolt=3.5
+            self.CellFullChrgCrnt=-self.Capacity/2
+
+            self.OcvInflexionBelow=3.281
+            self.OcvInflexion2=3.303
+            self.OcvInflexion3=3.343
+            self.OcvInflexionAbove=3.36
+            self.SocInflexion1=30
+            self.SocInflexion2=60
+            self.SocInflexion3=70
+
+            self.FullChrgSoc=100
+            self.PeakSoc=62.5
+            self.PeakVoltLowLmt=3.35
+            self.PeakVoltUpLmt=3.4
+            self.PeakCellVolt=[3.384,3.385,3.386,3.387,3.388]
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+
+            self.LookTab_SOC = [0,	5,	10,	15,	20,	25,	30,	35,	40,	45,	50,	55,	60,	61,	62,	63,	64,	65,	66,	67,	68,	69,	70,	75,	80,	85,	90,	95,	96,	97,	98,	99,	100]
+            self.LookTab_OCV = [2.6748,	3.2020,	3.2170,	3.2430,	3.2600,	3.2770,	3.2880,	3.2890,	3.2900,	3.2910,	3.2911,	3.2970,	3.3030,	3.3080,	3.3200,	3.3270,	3.3290,	3.3300,	3.3301,	3.3310,	3.3311,	3.3312,	3.3313,	3.3314,	3.3315,	3.3316,	3.3320,	3.3330,	3.3340,	3.3350,	3.3360,	3.3580,	3.3640]
+            self.LookTab_OCVChg=[2.6460, 3.1570, 3.2220, 3.2450,3.2760,	3.2970,	3.3060,	3.3070,	3.3080,	3.3090,	3.3110,	3.3140,	3.3220,	3.3270,	3.3330,	3.3380,	3.3420,	3.3421,	3.3422,	3.3423,	3.3424,	3.3431,	3.3432,	3.3433,	3.3434,	3.3441,	3.3442,	3.3443,	3.3444,	3.3445,	3.3446,	3.3447,	3.3640]
+
+            self.CellOvLv1=3.65
+            self.CellOvLv2=3.7
+            self.CellUvLv1=2.7
+            self.CellUvLv2=2.6
+            self.CellVoltDiffLv1=0.6
+            self.CellVoltDiffLv2=1.1
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=15
+            self.CellTempDiffLv2=20
+            self.CellTempRate=5
+
+            self.PackChgOc=-160
+            self.PackDisOc=180
+
+            self.LeakCurrentLv1=-20
+            self.LeakCurrentLv2=-30
+            self.LeakCurrentLv3=-100
+
+            self.TrwVoltRate=-8  
+            self.mk_slope=-0.8
+        
+
+        else:
+            print('未找到对应电池编号！！！')
+            # sys.exit()

USER/SPF/alibaba/04BatSoc/CBMSBatSoc.py

@@ -127,8 +127,10 @@ class BatSoc():
                         standingtime=0
                         if step<120:
                             as_accum=as_accum-self.packcrnt[i]*step
-                        else:
+                        elif 1<self.bms_soc[i]<=100 and 1<self.df_ram_sn.iloc[-1]['bms_soc']<=100:
                             as_accum=as_accum+((self.bms_soc[i]-self.df_ram_sn.iloc[-1]['bms_soc'])*capacity*3600/100)
+                        else:
+                            pass
                 else:
                     step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
                     if abs(self.packcrnt[i])<0.1 and abs(self.packcrnt[i-1])<0.1:
@@ -255,7 +257,10 @@ class BatSoc():
         #获取电池包SOH................................................................................................
         if self.df_soh.empty:
             batsoh=self.bms_soh[0]
-            capacity=self.param.Capacity*batsoh/100
+            if batsoh>30:
+                capacity=self.param.Capacity*batsoh/100
+            else:
+                capacity=self.param.Capacity
         else:
             batsoh=self.df_soh.loc[0,'soh']
             capacity=self.param.Capacity*batsoh/100
@@ -274,8 +279,10 @@ class BatSoc():
                         standingtime=0
                         if step<120:
                             as_accum=as_accum-self.packcrnt[i]*step
-                        else:
+                        elif 1<self.bms_soc[i]<=100 and 1<self.df_ram_sn.iloc[-1]['bms_soc']<=100:
                             as_accum=as_accum+((self.bms_soc[i]-self.df_ram_sn.iloc[-1]['bms_soc'])*capacity*3600/100)
+                        else:
+                            pass
                 else:
                     step=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
                     if abs(self.packcrnt[i])<0.1 and abs(self.packcrnt[i-1])<0.1:
@@ -308,7 +315,7 @@ class BatSoc():
             pass
 
         #权重值计算.................................................
-        ocvweight=ocvweight-as_accum*10/(capacity*3600)
+        ocvweight=ocvweight-(as_accum*10)/(capacity*3600)
         if ocvweight>1:
             ocvweight=1
         elif ocvweight<0:

USER/SPF/alibaba/04BatSoc/main.py

@@ -0,0 +1,128 @@
+import datetime
+import pandas as pd
+import numpy as np
+import time
+from apscheduler.schedulers.blocking import BlockingScheduler
+from USER.SPF.alibaba.Common import log
+import CBMSBatSoc
+
+#...................................SOC计算函数......................................................................................................................
+def soc_cal():
+    global df_ram
+    global SNnums
+    global df_bms
+    global now_time
+
+    end_time=now_time
+    start_time=now_time-datetime.timedelta(seconds=11)
+    now_time=now_time+datetime.timedelta(seconds=10)
+    # start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    # end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    for sn in SNnums:
+        # if 'PK500' in sn:
+        #     celltype=1 #6040三元电芯
+        # elif 'PK502' in sn:
+        #     celltype=2 #4840三元电芯
+        # elif 'K504B' in sn:
+        #     celltype=99    #60ah林磷酸铁锂电芯
+        # elif 'MGMLXN750' in sn:
+        #     celltype=3 #力信50ah三元电芯
+        # elif 'MGMCLN750' or 'UD' in sn: 
+        #     celltype=4 #CATL 50ah三元电芯
+        # else:
+        #     print('SN：{}，未找到对应电池类型！！！'.format(sn))
+        #     continue
+        #     sys.exit()
+        celltype=99
+        #读取原始数据库数据........................................................................................................................................................
+        df_bms1= df_bms[(df_bms['time']>=start_time) & (df_bms['time']<end_time)]
+        df_bms1=df_bms1.reset_index(drop=True)
+        if not df_bms1.empty:
+            cellvolt_name=['CellVolt'+str(x) for x in range(1,73)]
+            celltemp_name=['CellTemp'+str(x) for x in range(1,33)]
+            df_volt=pd.DataFrame([x[0].split(",") for x in np.array(df_bms1[['cell_volt']])])
+            df_volt.columns = cellvolt_name
+            df_volt=df_volt.astype('float')
+            df_temp=pd.DataFrame([x[0].split(",") for x in np.array(df_bms1[['cell_temp']])])
+            df_temp.columns = celltemp_name
+            df_temp=df_temp.astype('float')
+            df_bms1=pd.concat([df_bms1,df_volt,df_temp],axis=1)
+            df_bms1.dropna(axis=0, inplace=True)
+            df_bms1=df_bms1.reset_index(drop=True)
+
+
+        #读取结果数据库数据........................................................................................................................................................
+        # host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+        # port=3306
+        # db='qx_cas'
+        # user='qx_read'
+        # password='Qx@123456'
+        # mode=1
+        # tablename='cellstateestimation_soh'
+        # DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
+        # with DBRead as DBRead:
+        #     df_soh=DBRead.getdata(param='time_st,sn,soh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+        df_soh=pd.DataFrame()
+
+        # if celltype>50:
+        #     tablename='cellstateestimation_uniform_socvoltdiff'
+        #     with DBRead as DBRead:
+        #         df_socdiff=DBRead.getdata(param='time,sn,cellsoc_diff', tablename=tablename, sn=sn, timename='time', st=start_time, sp=end_time)
+        # else:
+        #     df_socdiff=pd.DataFrame()
+        df_socdiff=pd.DataFrame()
+
+        # print(df_bms)
+        # print(df_soh)
+        
+        #socram...............................................................................................................................................................
+        if not df_bms1.empty:
+            df_ram_sn=df_ram[df_ram['sn']==sn]
+            if df_ram_sn.empty:
+                with open(r'D:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\\'+'soc_'+sn+'.txt','a') as file:
+                    file.write(str(tuple(df_ram_sn.columns))+'\n')
+            
+            BatSoc=CBMSBatSoc.BatSoc(sn,celltype,df_bms1,df_soh,df_ram_sn,df_socdiff)
+            df_res, df_ram_sn=BatSoc.batsoc()
+            if not df_ram_sn.empty:
+                sn_index=df_ram.loc[df_ram['sn']==sn].index
+                df_ram=df_ram.drop(index=sn_index)
+                df_ram=df_ram.append(df_ram_sn)
+                df_ram.reset_index(inplace=True,drop=True)     #重置索引
+            
+            #结果存入数据库...........................................................
+            if not df_ram_sn.empty:
+                with open(r'D:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\\'+'soc_'+sn+'.txt','a') as file:
+                    file.write(str(tuple(df_ram_sn.iloc[-1][['time','sn','bms_soc', 'soc']]))+'\n')
+
+#...............................................主函数.......................................................................................................................
+if __name__ == "__main__":
+
+    #所有SN号
+    SNnums= ['LS002']
+
+    #读取原始数据库数据........................................................................................................................................................
+    df_bms= pd.read_excel(r'D:\01WorkSpace\项目\Alibaba\data\原始数据_2022-01-10_2022-02-10\力神_LS002.xlsx')
+    now_time=datetime.datetime.strptime('2022-4-27 10:05:00','%Y-%m-%d %H:%M:%S')
+
+    #log信息配置
+    mylog=log.Mylog('log_soc.txt','error')
+    mylog.logcfg()
+
+    #参数初始化.........................................................................................................................................................................
+    column_name=['time', 'sn', 'bms_soc', 'soc','cellsoc','standingtime','rampackcrnt','ramcellvolt','kocellvoltmin','kocellvoltmax','ocvweight','as_accum','socstep']
+    df_ram=pd.DataFrame(columns=column_name)  
+
+
+
+    scheduler = BlockingScheduler()
+    scheduler.add_job(soc_cal, 'interval', seconds=0.05, id='soc_job')
+    
+    try:
+        scheduler.start()
+
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/04BatSoc/soc_KPD001.txt

@@ -0,0 +1,38808 @@
+('time', 'sn', 'bms_soc', 'soc', 'cellsoc', 'standingtime', 'rampackcrnt', 'ramcellvolt', 'kocellvoltmin', 'kocellvoltmax', 'ocvweight', 'as_accum', 'socstep')
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USER/SPF/alibaba/04BatSoc/soc_LS001.txt

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+(Timestamp('2022-04-27 17:14:49'), 'LS002', 66.4, 68.8)
+(Timestamp('2022-04-27 17:14:57'), 'LS002', 66.4, 68.8)
+(Timestamp('2022-04-27 17:15:09'), 'LS002', 66.4, 68.8)
+(Timestamp('2022-04-27 17:15:17'), 'LS002', 66.4, 68.8)
+(Timestamp('2022-04-27 17:15:28'), 'LS002', 66.4, 68.7)
+(Timestamp('2022-04-27 17:15:39'), 'LS002', 66.4, 68.7)
+(Timestamp('2022-04-27 17:15:48'), 'LS002', 66.4, 68.7)
+(Timestamp('2022-04-27 17:15:59'), 'LS002', 66.4, 68.7)
+(Timestamp('2022-04-27 17:16:07'), 'LS002', 66.4, 68.7)
+(Timestamp('2022-04-27 17:16:19'), 'LS002', 66.4, 68.6)
+(Timestamp('2022-04-27 17:16:27'), 'LS002', 66.4, 68.6)
+(Timestamp('2022-04-27 17:16:38'), 'LS002', 66.4, 68.6)
+(Timestamp('2022-04-27 17:16:49'), 'LS002', 66.0, 68.6)
+(Timestamp('2022-04-27 17:16:57'), 'LS002', 66.0, 68.6)
+(Timestamp('2022-04-27 17:17:09'), 'LS002', 66.0, 68.5)
+(Timestamp('2022-04-27 17:17:17'), 'LS002', 66.0, 68.5)
+(Timestamp('2022-04-27 17:17:28'), 'LS002', 66.0, 68.5)
+(Timestamp('2022-04-27 17:17:38'), 'LS002', 66.0, 68.5)
+(Timestamp('2022-04-27 17:17:49'), 'LS002', 66.0, 68.5)
+(Timestamp('2022-04-27 17:17:57'), 'LS002', 66.0, 68.4)
+(Timestamp('2022-04-27 17:18:09'), 'LS002', 66.0, 68.4)
+(Timestamp('2022-04-27 17:18:17'), 'LS002', 66.0, 68.4)
+(Timestamp('2022-04-27 17:18:28'), 'LS002', 66.0, 68.4)
+(Timestamp('2022-04-27 17:18:39'), 'LS002', 66.0, 68.4)
+(Timestamp('2022-04-27 17:18:48'), 'LS002', 66.0, 68.4)
+(Timestamp('2022-04-27 17:18:59'), 'LS002', 66.0, 68.3)
+(Timestamp('2022-04-27 17:19:07'), 'LS002', 66.0, 68.3)
+(Timestamp('2022-04-27 17:19:18'), 'LS002', 66.0, 68.3)
+(Timestamp('2022-04-27 17:19:28'), 'LS002', 66.0, 68.3)
+(Timestamp('2022-04-27 17:19:39'), 'LS002', 66.0, 68.3)
+(Timestamp('2022-04-27 17:19:47'), 'LS002', 66.0, 68.2)
+(Timestamp('2022-04-27 17:19:59'), 'LS002', 66.0, 68.2)
+(Timestamp('2022-04-27 17:20:07'), 'LS002', 65.6, 68.2)
+(Timestamp('2022-04-27 17:20:18'), 'LS002', 65.6, 68.2)
+(Timestamp('2022-04-27 17:20:28'), 'LS002', 65.6, 68.2)
+(Timestamp('2022-04-27 17:20:39'), 'LS002', 65.6, 68.1)
+(Timestamp('2022-04-27 17:20:47'), 'LS002', 65.6, 68.1)
+(Timestamp('2022-04-27 17:20:59'), 'LS002', 65.6, 68.1)
+(Timestamp('2022-04-27 17:21:07'), 'LS002', 65.6, 68.1)
+(Timestamp('2022-04-27 17:21:18'), 'LS002', 65.6, 68.1)
+(Timestamp('2022-04-27 17:21:29'), 'LS002', 65.6, 68.0)
+(Timestamp('2022-04-27 17:21:38'), 'LS002', 65.6, 68.0)
+(Timestamp('2022-04-27 17:21:49'), 'LS002', 65.6, 68.0)
+(Timestamp('2022-04-27 17:21:57'), 'LS002', 65.6, 68.0)
+(Timestamp('2022-04-27 17:22:08'), 'LS002', 65.6, 68.0)
+(Timestamp('2022-04-27 17:22:18'), 'LS002', 65.6, 67.9)
+(Timestamp('2022-04-27 17:22:29'), 'LS002', 65.6, 67.9)
+(Timestamp('2022-04-27 17:22:37'), 'LS002', 65.6, 67.9)
+(Timestamp('2022-04-27 17:22:49'), 'LS002', 65.6, 67.9)
+(Timestamp('2022-04-27 17:22:57'), 'LS002', 65.6, 67.9)
+(Timestamp('2022-04-27 17:23:08'), 'LS002', 65.6, 67.8)
+(Timestamp('2022-04-27 17:23:18'), 'LS002', 65.6, 67.8)
+(Timestamp('2022-04-27 17:23:29'), 'LS002', 65.2, 67.8)
+(Timestamp('2022-04-27 17:23:37'), 'LS002', 65.2, 67.8)
+(Timestamp('2022-04-27 17:23:49'), 'LS002', 65.2, 67.8)
+(Timestamp('2022-04-27 17:23:57'), 'LS002', 65.2, 67.7)
+(Timestamp('2022-04-27 17:24:08'), 'LS002', 65.2, 67.7)
+(Timestamp('2022-04-27 17:24:18'), 'LS002', 65.2, 67.7)
+(Timestamp('2022-04-27 17:24:29'), 'LS002', 65.2, 67.7)
+(Timestamp('2022-04-27 17:24:37'), 'LS002', 65.2, 67.7)
+(Timestamp('2022-04-27 17:24:49'), 'LS002', 65.2, 67.6)
+(Timestamp('2022-04-27 17:24:57'), 'LS002', 65.2, 67.6)
+(Timestamp('2022-04-27 17:25:08'), 'LS002', 65.2, 67.6)
+(Timestamp('2022-04-27 17:25:19'), 'LS002', 65.2, 67.6)
+(Timestamp('2022-04-27 17:25:28'), 'LS002', 65.2, 67.6)
+(Timestamp('2022-04-27 17:25:39'), 'LS002', 65.2, 67.6)
+(Timestamp('2022-04-27 17:25:47'), 'LS002', 65.2, 67.5)
+(Timestamp('2022-04-27 17:25:58'), 'LS002', 65.2, 67.5)
+(Timestamp('2022-04-27 17:26:08'), 'LS002', 65.2, 67.5)
+(Timestamp('2022-04-27 17:26:19'), 'LS002', 65.2, 67.5)
+(Timestamp('2022-04-27 17:26:27'), 'LS002', 65.2, 67.5)
+(Timestamp('2022-04-27 17:26:39'), 'LS002', 65.2, 67.4)
+(Timestamp('2022-04-27 17:26:47'), 'LS002', 65.2, 67.4)
+(Timestamp('2022-04-27 17:26:58'), 'LS002', 64.8, 67.4)
+(Timestamp('2022-04-27 17:27:08'), 'LS002', 64.8, 67.4)
+(Timestamp('2022-04-27 17:27:19'), 'LS002', 64.8, 67.4)
+(Timestamp('2022-04-27 17:27:27'), 'LS002', 64.8, 67.4)
+(Timestamp('2022-04-27 17:27:39'), 'LS002', 64.8, 67.3)
+(Timestamp('2022-04-27 17:27:47'), 'LS002', 64.8, 67.3)
+(Timestamp('2022-04-27 17:27:58'), 'LS002', 64.8, 67.3)
+(Timestamp('2022-04-27 17:28:08'), 'LS002', 64.8, 67.3)
+(Timestamp('2022-04-27 17:28:14'), 'LS002', 64.8, 67.3)
+('time', 'sn', 'bms_soc', 'soc', 'cellsoc', 'standingtime', 'rampackcrnt', 'ramcellvolt', 'kocellvoltmin', 'kocellvoltmax', 'ocvweight', 'as_accum', 'socstep')
+(Timestamp('2022-04-27 10:03:49'), 'LS002', 4.4, 3.4)
+(Timestamp('2022-04-27 10:03:57'), 'LS002', 4.4, 3.4)
+(Timestamp('2022-04-27 10:04:08'), 'LS002', 4.4, 3.4)
+(Timestamp('2022-04-27 10:04:18'), 'LS002', 4.8, 3.4)
+(Timestamp('2022-04-27 10:04:29'), 'LS002', 4.8, 3.5)
+(Timestamp('2022-04-27 10:04:37'), 'LS002', 4.8, 3.5)
+(Timestamp('2022-04-27 10:04:49'), 'LS002', 4.8, 3.6)
+(Timestamp('2022-04-27 10:04:57'), 'LS002', 4.8, 3.7)
+(Timestamp('2022-04-27 10:05:08'), 'LS002', 4.8, 3.8)
+(Timestamp('2022-04-27 10:05:15'), 'LS002', 4.8, 3.9)
+('time', 'sn', 'bms_soc', 'soc', 'cellsoc', 'standingtime', 'rampackcrnt', 'ramcellvolt', 'kocellvoltmin', 'kocellvoltmax', 'ocvweight', 'as_accum', 'socstep')
+(Timestamp('2022-04-27 10:04:58'), 'LS002', 4.8, 5.8)
+(Timestamp('2022-04-27 10:05:08'), 'LS002', 4.8, 5.9)
+(Timestamp('2022-04-27 10:05:15'), 'LS002', 4.8, 5.9)
+('time', 'sn', 'bms_soc', 'soc', 'cellsoc', 'standingtime', 'rampackcrnt', 'ramcellvolt', 'kocellvoltmin', 'kocellvoltmax', 'ocvweight', 'as_accum', 'socstep')
+(Timestamp('2022-04-27 10:04:58'), 'LS002', 4.8, 5.8)
+(Timestamp('2022-04-27 10:05:08'), 'LS002', 4.8, 5.9)
+(Timestamp('2022-04-27 10:05:15'), 'LS002', 4.8, 5.9)

USER/SPF/alibaba/05BatDiag/CBMSBatDiag.py

@@ -261,7 +261,7 @@ class BatDiag:
             if cellvoltvalid==1:
                 #过压诊断.............................................................................................................
                 if not 12 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
-                    if cellvoltmax0>self.param.CellOvLv2 and cellvoltmax1>self.param.CellOvLv2:  #二级过压进入
+                    if cellvoltmax0>self.param.CellOvLv2 and cellvoltmax1>self.param.CellOvLv2 and self.packcrnt[i]<-0.5:  #二级过压进入
                         cov_time=cov_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
                         if cov_time>self.param.volt_time:
                             faultcode=12
@@ -333,7 +333,7 @@ class BatDiag:
 
             if packvoltvalid==1:
                 if not 18 in list(self.df_diag_ram['faultcode']):  #当前故障中没有该故障，则判断是否发生该故障
-                    if self.packvolt[i-1]>self.param.PackVoltOvLv2 and self.packvolt[i]>self.param.PackVoltOvLv2:   #电池包过压二级进入
+                    if self.packvolt[i-1]>self.param.PackVoltOvLv2 and self.packvolt[i]>self.param.PackVoltOvLv2 and self.packcrnt[i]<-0.5:   #电池包过压二级进入
                         pov_time=pov_time+(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
                         if pov_time>self.param.volt_time:
                             faultcode=18
@@ -620,10 +620,10 @@ class BatDiag:
             healthstate=healthstate-30
             healthadvice=healthadvice+'，电池发生{}个故障'.format(fault_num)
         elif fltlv2>0:
-            healthstate=healthstate-20
+            healthstate=healthstate-15
             healthadvice=healthadvice+'，电池发生{}个故障'.format(fault_num)
         elif fltlv1>0:
-            healthstate=healthstate-10
+            healthstate=healthstate-5
             healthadvice=healthadvice+'，电池发生{}个故障'.format(fault_num)
 
         #电池寿命

USER/SPF/alibaba/07BatSafetyWarning/BatParam.py

@@ -0,0 +1,196 @@
+
+#定义电池参数
+
+class BatParam:
+    def __init__(self,celltype):
+
+        #公用参数................................................................................................................................................
+        #热失控参数
+        self.TrwTempHigh=60
+        self.TrwTempRate=3
+        self.TrwTempDiff=15
+        self.TrwCellVoltDiff=2.5
+        self.TrwCellVoltFall=0.5
+        self.TrwCellVoltLow=1.5
+        self.TrwPackVoltFall=1.5
+        
+        #故障诊断参数
+        self.temp_time=10
+        self.volt_time=10
+
+        self.SocJump=10
+        self.SocClamp=0.1
+        self.SocLow=3
+        self.SocDiff=20
+
+        self.SohLow=65
+        self.SohDiff=15
+
+        #mana-kendall趋势检验参数
+        self.mk_p=0.001
+        self.mk_z=-5
+        self.mk_Tau=-0.7
+        self.mk_slope=-0.1
+        self.mk_s=-260
+        self.mk_svar=1600
+
+        self.OcvWeight_Temp=[-30,-20,-10,0,10,20,30,40,50]
+        self.OcvWeight_StandingTime=[0,500,600,1200,1800,3600,7200,10800]
+        self.OcvWeight            =[[0,0,  0,  0,    0,   0.1,0.3, 1],
+                                    [0,0,  0,  0,    0,   0.1,0.3, 1],
+                                    [0,0,  0,  0,    0,   0.2,0.5, 1],
+                                    [0,0,  0,  0,    0.2, 0.4,0.8, 1],
+                                    [0,0,  0,  0.1,  0.3, 0.6,1,   1],
+                                    [0,0,  0.1,0.2,  0.5, 0.8,1,   1],
+                                    [0,0,  0.1,0.3,  0.6, 1,  1,   1],
+                                    [0,0,  0.1,0.3,  0.7, 1,  1,   1],
+                                    [0,0,  0.2,0.3,  0.8, 1,  1,   1]]
+
+
+        if celltype==1: #永联+康普顿21700
+            self.Capacity = 4*2
+            self.PackFullChrgVolt=69.99
+            self.CellFullChrgVolt=4.2
+            self.CellFullChrgCrnt=-4
+            self.CellVoltNums=120
+            self.CellTempNums=40
+            self.FullChrgSoc=90
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+            self.LookTab_SOC = [0,	     5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100]
+            self.LookTab_OCV = [2.8497,	3.0924,	3.2868,	3.4117,	3.4461,	3.5032,	3.5602,	3.6049,	3.6368,	3.6709,	3.7121,	3.7593,	3.8237,	3.8755,	3.9093,	3.9518,	4.0119,	4.0615,	4.077,	4.0969,	4.1821]
+
+            self.CellOvLv1=4.2
+            self.CellOvLv2=4.25
+            self.CellUvLv1=2.8
+            self.CellUvLv2=2.5
+            self.CellVoltDiffLv1=0.3
+            self.CellVoltDiffLv2=0.5
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=10
+            self.CellTempDiffLv2=15
+            self.CellTempRate=5
+
+            self.PackChgOc=-40
+            self.PackDisOc=200
+
+            self.LeakCurrentLv1=-10
+            self.LeakCurrentLv2=-15
+            self.LeakCurrentLv3=-50
+
+            self.TrwVoltRate=-1
+
+        elif celltype==2: #中恒+华庭18650
+            self.Capacity = 2*9
+            self.CellVoltNums= 68
+            self.CellTempNums= 40
+            self.CellFullChrgVolt=4.2
+            self.CellFullChrgCrnt=-self.Capacity/2
+            self.FullChrgSoc=100
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+            self.LookTab_SOC = [0,	     5,	    10,	    15,	    20,	    25,	    30,	    35,	    40,	    45,	    50,	    55,	    60,	    65,	    70,	    75,	    80,	    85,	    90,	    95,	    100]
+            self.LookTab_OCV = [2.929,	3.2902,	3.3888,	3.425,	3.4846,	3.5382,	3.577,	3.6083,	3.638,	3.6786,	3.7127,	3.7596,	3.7853,	3.8371,	3.9081,	3.9127,	4.0097,	4.0314,	4.0463,	4.0969,	4.174]
+
+            self.CellOvLv1=4.04
+            self.CellOvLv2=4.05
+            self.CellUvLv1=2.99
+            self.CellUvLv2=2.950
+            self.CellVoltDiffLv1=0.35
+            self.CellVoltDiffLv2=0.4
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=10
+            self.CellTempDiffLv2=15
+            self.CellTempRate=5
+
+            self.PackChgOc=-45
+            self.PackDisOc=200
+
+            self.LeakCurrentLv1=-10
+            self.LeakCurrentLv2=-15
+            self.LeakCurrentLv3=-50
+
+            self.TrwVoltRate=-1
+
+        elif celltype==99:   #永联+力神
+            self.Capacity = 40
+            self.CellVoltNums=72
+            self.CellTempNums=32
+            self.CellFullChrgVolt=3.55
+            self.CellFullChrgCrnt=-self.Capacity/2
+
+            self.OcvInflexionBelow=3.281
+            self.OcvInflexion2=3.303
+            self.OcvInflexion3=3.343
+            self.OcvInflexionAbove=3.36
+            self.SocInflexion1=30
+            self.SocInflexion2=60
+            self.SocInflexion3=70
+
+            self.FullChrgSoc=100
+            self.PeakSoc=62.5
+            self.PeakVoltLowLmt=3.35
+            self.PeakVoltUpLmt=3.4
+            self.PeakCellVolt=[3.384,3.385,3.386,3.387,3.388]
+            self.PackCrntDec=1
+            self.BalCurrent=0.015
+
+            self.LookTab_SOC = [0,	5,	10,	15,	20,	25,	30,	35,	40,	45,	50,	55,	60,	61,	62,	63,	64,	65,	66,	67,	68,	69,	70,	75,	80,	85,	90,	95,	96,	97,	98,	99,	100]
+            self.LookTab_OCV = [2.6748,	3.2020,	3.2170,	3.2430,	3.2600,	3.2770,	3.2880,	3.2890,	3.2900,	3.2910,	3.2911,	3.2970,	3.3030,	3.3080,	3.3200,	3.3270,	3.3290,	3.3300,	3.3301,	3.3310,	3.3311,	3.3312,	3.3313,	3.3314,	3.3315,	3.3316,	3.3320,	3.3330,	3.3340,	3.3350,	3.3360,	3.3580,	3.3640]
+            self.LookTab_OCVChg=[2.6460, 3.1570, 3.2220, 3.2450,3.2760,	3.2970,	3.3060,	3.3070,	3.3080,	3.3090,	3.3110,	3.3140,	3.3220,	3.3270,	3.3330,	3.3380,	3.3420,	3.3421,	3.3422,	3.3423,	3.3424,	3.3431,	3.3432,	3.3433,	3.3434,	3.3441,	3.3442,	3.3443,	3.3444,	3.3445,	3.3446,	3.3447,	3.3640]
+
+            self.CellOvLv1=3.65
+            self.CellOvLv2=3.7
+            self.CellUvLv1=2.7
+            self.CellUvLv2=2.6
+            self.CellVoltDiffLv1=0.6
+            self.CellVoltDiffLv2=1.1
+            self.PackVoltOvLv1=self.CellOvLv1*self.CellVoltNums
+            self.PackVoltOvLv2=self.CellOvLv2*self.CellVoltNums
+            self.PackVoltUvLv1=self.CellUvLv1*self.CellVoltNums
+            self.PackVoltUvLv2=self.CellUvLv2*self.CellVoltNums
+
+            self.CellTempUpLmt=119
+            self.CellTempLwLmt=-39
+            self.CellTempHighLv1=45
+            self.CellTempHighLv2=50
+            self.CellTempLowLv1=0
+            self.CellTempLowLv2=-5
+            self.CellTempDiffLv1=15
+            self.CellTempDiffLv2=20
+            self.CellTempRate=5
+
+            self.PackChgOc=-160
+            self.PackDisOc=180
+
+            self.LeakCurrentLv1=-20
+            self.LeakCurrentLv2=-30
+            self.LeakCurrentLv3=-100
+
+            self.TrwVoltRate=-8  
+            self.mk_slope=-0.8
+        
+
+        else:
+            print('未找到对应电池编号！！！')
+            # sys.exit()

USER/SPF/alibaba/07BatSafetyWarning/CBMSBatInterShort.py

@@ -7,8 +7,8 @@ class BatInterShort():
     def __init__(self,sn,celltype,df_bms,df_soh,df_last,df_last1,df_last2,df_last3,df_lfp):  #参数初始化
 
         if (not df_lfp.empty) and celltype>50:
-            df_lfp.drop(['sn'],axis=1)
             df_bms=pd.concat([df_lfp, df_bms], ignore_index=True)
+            df_bms.reset_index(inplace=True,drop=True) 
         else:
             pass
 
@@ -84,17 +84,21 @@ class BatInterShort():
         time_last=time_now-datetime.timedelta(seconds=300)
         df_volt=self.df_bms[(self.df_bms['time']>=time_last) & (self.df_bms['time']<=time_now)]
         df_volt=df_volt[self.cellvolt_name]
+        df_volt=df_volt[(df_volt>2) & (df_volt<4.5)]
+        df_volt=df_volt.dropna()
         cellvolt_std=df_volt.std(axis=0)
-        if len(df_volt)>2 and max(cellvolt_std)<1.5:
+        if len(df_volt)>2 and max(cellvolt_std)<0.005:
             cellvolt_sum=df_volt.sum(0)-df_volt.max(0)-df_volt.min(0)
             cellvolt_mean=cellvolt_sum/(len(df_volt)-2)
             cellvolt=cellvolt_mean
         elif len(df_volt)==2:
             # df_volt=pd.DataFrame(df_volt,dtype=np.float)
-            if max(abs(df_volt.iloc[1]-df_volt.iloc[0]))<3:
+            if max(abs(df_volt.iloc[1]-df_volt.iloc[0]))<0.003:
                 cellvolt=df_volt.mean(0)
             else:
                 cellvolt=pd.DataFrame()
+        elif len(df_volt)==1:
+            cellvolt=df_volt.iloc[0]
         else:
             cellvolt=pd.DataFrame()
         return cellvolt
@@ -471,7 +475,7 @@ class BatInterShort():
             else:
                 pass
 
-            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1:
                 delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
                 standingtime=standingtime+delttime
                 standingtime1=standingtime1+delttime
@@ -480,9 +484,9 @@ class BatInterShort():
                 #长时间静置法计算内短路-开始.....................................................................................................................................
                 if firsttime==1:
                     if standingtime>self.StandardStandingTime:      #静置时间满足要求
-                        standingtime=0
                         cellvolt_now=self._avgvolt_get(i)
                         if not cellvolt_now.empty:
+                            standingtime=0
                             cellvolt_min=min(cellvolt_now)
                             cellvolt_max=max(cellvolt_now)
                             # cellvolt_last=self._avgvolt_get(i-1)
@@ -501,9 +505,9 @@ class BatInterShort():
                     else:
                         pass
                 elif standingtime>3600*12:
-                    standingtime=0
                     cellvolt_now=np.array(self._avgvolt_get(i))
                     if not cellvolt_now.empty:
+                        standingtime=0
                         cellvolt_min=min(cellvolt_now)
                         cellvolt_max=max(cellvolt_now)
                         # cellvolt_last=np.array(self._avgvolt_get(i-1))
@@ -550,9 +554,9 @@ class BatInterShort():
 
                 #非平台区间静置法计算内短路-开始.....................................................................................................................................................
                 if standingtime1>self.StandardStandingTime:
-                    standingtime1=0
                     cellvolt_now1=self._avgvolt_get(i)
                     if not cellvolt_now1.empty:
+                        standingtime1=0
                         cellvolt_max1=max(cellvolt_now1)
                         cellvolt_min1=min(cellvolt_now1)
                         # cellvolt_last1=self._avgvolt_get(i-1)
@@ -560,7 +564,7 @@ class BatInterShort():
                         cellsoc_max1=np.interp(cellvolt_max1,self.param.LookTab_OCV,self.param.LookTab_SOC)
                         cellsoc_min1=np.interp(cellvolt_min1,self.param.LookTab_OCV,self.param.LookTab_SOC)
 
-                        if cellsoc_max1<self.param.SocInflexion1-2 and 12<cellsoc_min1:
+                        if cellsoc_max1<self.param.SocInflexion1-2 and 1<cellsoc_min1:
                             if firsttime1==1:
                                 dict_baltime1=self._bal_time(dict_bal1)   #获取每个电芯的均衡时间
                                 deltsoc_last1, cellsoc_last1=self._celldeltsoc_get(cellvolt_now1,dict_baltime1,capacity)
@@ -600,6 +604,11 @@ class BatInterShort():
                 standingtime1=0
                 pass
 
+            if i==len(self.df_bms)-2 and abs(self.packcrnt[i+1]) < 0.1:  #数据中断后仍在静置，将最后一条数据写入RAM
+                df_ram_lfp.loc[0]=self.df_bms.iloc[-1]
+            else:
+                pass
+
             #获取充电数据——开始..............................................................................................................
             try:
                 balstat=int(self.df_bms.loc[i,'单体均衡状态'])  #统计均衡状态
@@ -616,11 +625,11 @@ class BatInterShort():
                 dict_bal2={}
 
             #判断充电状态
-            if self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i-1]<=-1:
-                if charging==0:
+            if charging==0:
+                if self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i-1]<=-1:
                     if self.bms_soc[i]<40:
                         cellvolt_now=self._cellvolt_get(i)
-                        if min(cellvolt_now)<self.param.CellFullChrgVolt-0.15:
+                        if min(cellvolt_now)<self.param.CellFullChrgVolt-0.17:
                             charging=1
                             chrg_start=i
                         else:
@@ -628,51 +637,49 @@ class BatInterShort():
                     else:
                         pass
 
-                else: #充电中
-                    cellvolt_now=self._cellvolt_get(i)
-                    if (self.bmstime[i+1]-self.bmstime[i]).total_seconds()>180 or (self.packcrnt[i]>self.param.Capacity/3 and self.packcrnt[i+1]>self.param.Capacity/3):  #如果充电过程中时间间隔>180s，则舍弃该次充电
+            else: #充电中
+                cellvolt_now=self._cellvolt_get(i)
+                if (self.bmstime[i+1]-self.bmstime[i]).total_seconds()>180 or (self.packcrnt[i]>-0.1 and self.packcrnt[i-1]>-0.1) or (self.packcrnt[i]<-self.param.Capacity and self.packcrnt[i+1]<-self.param.Capacity):  #如果充电过程中时间间隔>180s，则舍弃该次充电
+                    charging=0
+                    continue
+                elif min(cellvolt_now)>self.param.CellFullChrgVolt-0.1:   
+                    self._celltemp_weight(i)
+                    if i-chrg_start>10 and self.celltemp>20:
+                        chrg_end=i+1
                         charging=0
-                        continue
-                    elif min(cellvolt_now)>self.param.CellFullChrgVolt-0.13:   #电压>满充电压-0.13V，即3.37V
-                        self._celltemp_weight(i)
-                        if i-chrg_start>10 and self.celltemp>20:
-                            chrg_end=i+1
-                            charging=0
-
-                            #计算漏电流值...................................................................
-                            if firsttime2==1:
-                                dict_baltime={}
-                                deltAs_last2=self._cellDeltAs_get(chrg_start,chrg_end,dict_baltime)
-                                time_last2=self.bmstime[chrg_end]
-                                df_ram_last2.loc[0]=[self.sn,time_last2,deltAs_last2]    #更新RAM信息
-                            else:
-                                dict_baltime=self._bal_time(dict_bal2)   #获取每个电芯的均衡时间
-                                deltAs_now2=self._cellDeltAs_get(chrg_start,chrg_end,dict_baltime)  #获取每个电芯的As差
-                                time_now2=self.bmstime[chrg_end]
-                                df_ram_last2.loc[0]=[self.sn,time_now2,deltAs_now2]    #更新RAM信息
-
-                                list_sub2=deltAs_now2-deltAs_last2
-                                list_pud2=-1000/(time_now2-time_last2).total_seconds()
-                                leak_current2=list_sub2*list_pud2
-                                # leak_current=np.array(leak_current)
-                                leak_current2=np.round(leak_current2,3)
-                                leak_current2=list(leak_current2)
-
-                                df_res.loc[len(df_res)]=[time_last2,time_now2,self.sn,3,str(leak_current2),str(dict_baltime)]  #计算结果存入Dataframe
-                                deltAs_last2=deltAs_now2
-                                time_last2=time_now2
-                                dict_bal2={}
 
+                        #计算漏电流值...................................................................
+                        if firsttime2==1:
+                            dict_baltime={}
+                            deltAs_last2=self._cellDeltAs_get(chrg_start,chrg_end,dict_baltime)
+                            time_last2=self.bmstime[chrg_end]
+                            df_ram_last2.loc[0]=[self.sn,time_last2,deltAs_last2]    #更新RAM信息
                         else:
-                            charging=0
-                            continue
-                    elif i==len(self.df_bms)-2:  #数据中断后仍在充电，将前段充电数据写入RAM
-                        df_ram_lfp=self.df_bms.iloc[chrg_start:]
-                        df_ram_lfp['sn']=self.sn
+                            dict_baltime=self._bal_time(dict_bal2)   #获取每个电芯的均衡时间
+                            deltAs_now2=self._cellDeltAs_get(chrg_start,chrg_end,dict_baltime)  #获取每个电芯的As差
+                            time_now2=self.bmstime[chrg_end]
+                            df_ram_last2.loc[0]=[self.sn,time_now2,deltAs_now2]    #更新RAM信息
+
+                            list_sub2=deltAs_now2-deltAs_last2
+                            list_pud2=-1000/(time_now2-time_last2).total_seconds()
+                            leak_current2=list_sub2*list_pud2
+                            # leak_current=np.array(leak_current)
+                            leak_current2=np.round(leak_current2,3)
+                            leak_current2=list(leak_current2)
+
+                            df_res.loc[len(df_res)]=[time_last2,time_now2,self.sn,3,str(leak_current2),str(dict_baltime)]  #计算结果存入Dataframe
+                            deltAs_last2=deltAs_now2
+                            time_last2=time_now2
+                            dict_bal2={}
+
                     else:
-                        pass
-            else:
-                pass
+                        charging=0
+                        continue
+                elif i==len(self.df_bms)-2:  #数据中断后仍在充电，将前段充电数据写入RAM
+                    df_ram_lfp=self.df_bms.iloc[chrg_start:]
+                    df_ram_lfp['sn']=self.sn
+                else:
+                    pass
 
 
         #更新RAM

USER/SPF/alibaba/07BatSafetyWarning/CBMSBatUniform.py

@@ -7,8 +7,8 @@ class BatUniform():
     def __init__(self,sn,celltype,df_bms,df_uniform,df_last3,df_lfp1):  #参数初始化
 
         if (not df_lfp1.empty) and celltype>50:
-            df_lfp1.drop(['sn'],axis=1)
             df_bms=pd.concat([df_lfp1, df_bms], ignore_index=True)
+            df_bms.reset_index(inplace=True,drop=True)
         else:
             pass
 
@@ -156,14 +156,24 @@ class BatUniform():
         if len(df_Data1)>2:     #寻找峰值点，且峰值点个数>2
             PeakIndex = df_Data1['DVDQ'].idxmax()
             df_Data2 = df_Data1[(df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
-            if len(df_Data2) > 2 and df_Data1.loc[PeakIndex,'XVOLT']<self.param.PeakVoltUpLmt-0.019:
+            if len(df_Data2) > 1 and min(df_Data1['SOC'])+0.5<df_Data1['SOC'][PeakIndex]<max(df_Data1['SOC'])-1:
                 return df_Data1['AhSoc'][PeakIndex]
+            
             else:
-                df_Data1 = df_Data1.drop([PeakIndex])
-                PeakIndex = df_Data1['DVDQ'].idxmax()
-                df_Data2 = df_Data1[(df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
-                if len(df_Data2) > 2 and df_Data1.loc[PeakIndex,'XVOLT']<self.param.PeakVoltUpLmt-0.019:
-                    return df_Data1['AhSoc'][PeakIndex]
+                if min(df_Data1['SOC'])+0.5<df_Data1['SOC'][PeakIndex]<max(df_Data1['SOC'])-1:
+                    df_Data1=df_Data1.drop([PeakIndex])
+                elif df_Data1['SOC'][PeakIndex]>max(df_Data1['SOC'])-1:
+                    df_Data1=df_Data1[df_Data1['SOC']<(df_Data1['SOC'][PeakIndex]-1)]
+                else:
+                    df_Data1=df_Data1[df_Data1['SOC']>(df_Data1['SOC'][PeakIndex]+0.5)]
+                
+                if len(df_Data1)>2:
+                    PeakIndex = df_Data1['DVDQ'].idxmax()
+                    df_Data2 = df_Data1[(df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
+                    if len(df_Data2) > 1 and min(df_Data1['SOC'])+0.5<df_Data1['SOC'][PeakIndex]<max(df_Data1['SOC'])-1:
+                        return df_Data1['AhSoc'][PeakIndex]
+                    else:
+                        return 0
                 else:
                     return 0
         else:
@@ -189,15 +199,15 @@ class BatUniform():
                 pass
 
 
-        for i in range(1,len(self.df_bms)-2):
+        for i in range(1,len(self.df_bms)-1):
 
-            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     #电流为0
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1:     #电流为0
                 delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
                 standingtime2=standingtime2+delttime
                 self._celltemp_weight(i)     #获取不同温度对应的静置时间
 
                 if standingtime2>self.StandardStandingTime:      #静置时间满足要求
-                    if abs(self.packcrnt[i+2]) >= 0.1:
+                    if abs(self.packcrnt[i+1]) >= 0.1:
                         standingtime2=0
                         cellvolt_now=self._cellvolt_get(i)
                         cellvolt_min=min(cellvolt_now)
@@ -216,8 +226,7 @@ class BatUniform():
                             cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
                             cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
                             df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num, str(cellvolt_rank)]
-                    elif standingtime2>3600*6:
-                        standingtime2=0
+                    elif standingtime2>3600:
                         cellvolt_now=self._cellvolt_get(i)
                         cellvolt_min=min(cellvolt_now)
                         cellvolt_max=max(cellvolt_now)
@@ -225,6 +234,7 @@ class BatUniform():
                         deltvolt=max(abs(cellvolt_now-cellvolt_last))
 
                         if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.005:
+                            standingtime2=0
                             cellvolt_sort=np.argsort(cellvolt_now)
                             cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
                             cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
@@ -236,8 +246,7 @@ class BatUniform():
                             cellsoc_diff=eval(format(cellsoc_diff,'.1f'))
                             cellvolt_diff=eval(format(cellvolt_diff,'.0f'))
                             df_res.loc[len(df_res)]=[self.bmstime[i], self.sn, cellsoc_diff, cellvolt_diff, cellmin_num, cellmax_num, str(cellvolt_rank)]
-                    elif i>=len(self.df_bms)-3:
-                        standingtime2=0
+                    elif i>=len(self.df_bms)-2:
                         cellvolt_now=self._cellvolt_get(i)
                         cellvolt_min=min(cellvolt_now)
                         cellvolt_max=max(cellvolt_now)
@@ -245,6 +254,7 @@ class BatUniform():
                         deltvolt=max(abs(cellvolt_now-cellvolt_last))
 
                         if 3<cellvolt_min<4.5 and 3<cellvolt_max<4.5 and deltvolt<0.005:
+                            standingtime2=0
                             cellvolt_sort=np.argsort(cellvolt_now)
                             cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
                             cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
@@ -296,16 +306,16 @@ class BatUniform():
             else:
                 pass
 
-        for i in range(2,len(self.df_bms)-2):
+        for i in range(1,len(self.df_bms)-1):
 
             #静置电压法计算电芯一致性
-            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1 and abs(self.packcrnt[i+1]) < 0.1:     #电流为0
+            if abs(self.packcrnt[i]) < 0.1 and abs(self.packcrnt[i-1]) < 0.1:     #电流为0
                 delttime=(self.bmstime[i]-self.bmstime[i-1]).total_seconds()
                 standingtime2=standingtime2+delttime
                 self._celltemp_weight(i)     #获取不同温度对应的静置时间
 
                 if standingtime2>self.StandardStandingTime:      #静置时间满足要求
-                    if abs(self.packcrnt[i+2]) >= 0.1:
+                    if abs(self.packcrnt[i+1]) >= 0.1:
                         standingtime2=0
                         cellvolt_now=self._cellvolt_get(i)
                         cellvolt_min=min(cellvolt_now)
@@ -339,7 +349,6 @@ class BatUniform():
                         #     else:
                         #         pass
                     elif standingtime2>3600*6:
-                        standingtime2=0
                         cellvolt_now=self._cellvolt_get(i)
                         cellvolt_min=min(cellvolt_now)
                         cellvolt_max=max(cellvolt_now)
@@ -347,6 +356,7 @@ class BatUniform():
                         deltvolt=max(abs(cellvolt_now-cellvolt_last))
 
                         if 2 < cellvolt_max < self.param.OcvInflexionBelow-0.002 and 2<cellvolt_min<4.5 and deltvolt<0.005:
+                            standingtime2=0
                             cellvolt_sort=np.argsort(cellvolt_now)
                             cellvolt_rank=list(np.argsort(cellvolt_sort)+1)
                             cellmin_num=list(cellvolt_now).index(cellvolt_min)+1
@@ -372,7 +382,7 @@ class BatUniform():
                         #     else:
                         #         pass
 
-                    elif i>=len(self.df_bms)-3:
+                    elif i>=len(self.df_bms)-2:
                         standingtime2=0
                         cellvolt_now=self._cellvolt_get(i)
                         cellvolt_min=min(cellvolt_now)
@@ -412,9 +422,14 @@ class BatUniform():
                 standingtime2=0
                 pass
 
+            if i==len(self.df_bms)-2 and abs(self.packcrnt[i+1]) < 0.1:  #数据中断后仍在静置，将最后一条数据写入RAM
+                df_ram_lfp1.loc[0]=self.df_bms.iloc[-1]
+            else:
+                pass
+
             #获取DVDQ算法所需数据——开始............................................................................................................
             if charging==0: #判断充电开始
-                if self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and self.packcrnt[i+2]<=-1 and self.bms_soc[i]<40:     #充电开始
+                if self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and self.bms_soc[i]<40:     #充电开始
                     charging=1
                     if len(chrg_start)>len(chrg_end):
                         chrg_start[-1]=i
@@ -424,19 +439,19 @@ class BatUniform():
                     pass
 
             else: #充电中
-                if (self.bmstime[i+1]-self.bmstime[i]).total_seconds()>180 or (self.packcrnt[i]<-self.param.Capacity/2 and self.packcrnt[i+1]<-self.param.Capacity/2):  #如果充电过程中时间间隔>180s，则舍弃该次充电
+                if (self.bmstime[i+1]-self.bmstime[i]).total_seconds()>180 or (self.packcrnt[i]<-self.param.Capacity and self.packcrnt[i+1]<-self.param.Capacity):  #如果充电过程中时间间隔>180s，则舍弃该次充电
                     chrg_start.remove(chrg_start[-1])
                     charging=0
                     continue
-                elif self.packcrnt[i]<=-1 and self.packcrnt[i+1]<=-1 and  self.packcrnt[i+2]>-1:  #判断电流波动时刻
+                elif self.packcrnt[i]<=-1 and self.packcrnt[i-1]<=-1 and  self.packcrnt[i+1]>-1:  #判断电流波动时刻
                     cellvolt_now=self._cellvolt_get(i+1)
-                    if max(cellvolt_now)>self.param.CellFullChrgVolt:   #电压>满充电压
+                    if max(cellvolt_now)>self.param.CellFullChrgVolt-0.1:   #电压>满充电压
                         chrg_end.append(i+1)
                         charging=0
                         continue
                     else:
                         pass
-                elif self.packcrnt[i+1]>-0.1 and self.packcrnt[i+2]>-0.1:   #判断充电结束
+                elif self.packcrnt[i+1]>-0.1 and self.packcrnt[i]>-0.1:   #判断充电结束
                     charging=0
                     if len(chrg_start)>len(chrg_end):
                         if self.bms_soc[i]>90:
@@ -446,14 +461,13 @@ class BatUniform():
                             continue
                     else:
                         continue
-                elif i==len(self.packcrnt)-3 and self.packcrnt[i+1]<-1 and self.packcrnt[i+2]<-1:
+                elif i==len(self.packcrnt)-2 and self.packcrnt[i+1]<-1 and self.packcrnt[i]<-1:
                     charging=0
                     if len(chrg_start)>len(chrg_end) and self.bms_soc[i]>90:   #soc>90
                         chrg_end.append(i)
                         continue
                     else:
                         df_ram_lfp1=self.df_bms.iloc[chrg_start[-1]:]
-                        df_ram_lfp1['sn']=self.sn
                         chrg_start.remove(chrg_start[-1])
                         continue
                 else:

USER/SPF/alibaba/07BatSafetyWarning/内短路.txt

@@ -0,0 +1,10 @@
+              time_st             time_sp     sn method                                      short_current baltime
+0 2022-04-27 10:03:44 2022-04-28 11:47:32  LS001      2  [-3.377, -1.755, -4.264, -0.871, 2.08, 1.342, ...      {}
+              time_st             time_sp     sn method                                      short_current baltime
+0 2022-04-27 10:03:44 2022-04-28 11:47:32  LS001      2  [-3.377, -1.755, -4.264, -0.871, 2.08, 1.342, ...      {}
+              time_st             time_sp     sn method                                      short_current baltime
+0 2022-04-27 13:00:44 2022-04-28 14:47:20  LS002      3  [5.411, -10.122, 1.269, -2.334, -7.758, 2.321,...      {}
+              time_st             time_sp     sn method                                      short_current baltime
+0 2022-04-27 10:03:46 2022-04-28 11:47:34  LS002      2  [-2.073, -1.778, 2.352, -1.631, -0.45, -1.336,...      {}
+              time_st             time_sp     sn method                                      short_current baltime
+0 2022-04-27 13:00:44 2022-04-28 14:47:20  LS002      3  [5.411, -10.122, 1.269, -2.334, -7.758, 2.321,...      {}

USER/SPF/alibaba/07BatSafetyWarning/电压排名.txt

@@ -0,0 +1,20 @@
+                 time     sn  cellsoc_diff cellvolt_diff cellmin_num cellmax_num                                      cellvolt_rank
+0 2022-04-27 10:04:11  LS001           0.9           100          43          63  [47, 23, 3, 14, 33, 48, 49, 50, 60, 69, 53, 54...
+                 time     sn  cellsoc_diff cellvolt_diff cellmin_num cellmax_num                                      cellvolt_rank
+0 2022-04-28 11:47:33  LS001           2.4             8           3          63  [7, 28, 2, 31, 43, 41, 32, 39, 38, 66, 51, 53,...
+                 time     sn  cellsoc_diff cellvolt_diff cellmin_num cellmax_num                                      cellvolt_rank
+0 2022-04-28 11:47:33  LS001           2.4             8           3          63  [7, 28, 2, 31, 43, 41, 32, 39, 38, 66, 51, 53,...
+                 time     sn  cellsoc_diff cellvolt_diff cellmin_num cellmax_num                                      cellvolt_rank
+0 2022-04-28 11:47:33  LS001           2.4             8           3          63  [7, 28, 2, 31, 43, 41, 32, 39, 38, 66, 51, 53,...
+                 time     sn  cellsoc_diff cellvolt_diff cellmin_num cellmax_num                                      cellvolt_rank
+0 2022-04-28 11:47:33  LS001           2.4             8           3          63  [7, 28, 2, 31, 43, 41, 32, 39, 38, 66, 51, 53,...
+                 time     sn  cellsoc_diff cellvolt_diff cellmin_num cellmax_num                                      cellvolt_rank
+0 2022-04-27 11:38:36  LS002           1.5            53          33          71  [29, 30, 45, 35, 69, 26, 34, 32, 48, 37, 55, 3...
+                 time     sn  cellsoc_diff cellvolt_diff cellmin_num cellmax_num                                      cellvolt_rank
+0 2022-04-27 10:04:15  LS002           0.8            84          33          71  [39, 36, 42, 33, 69, 25, 31, 27, 46, 41, 51, 3...
+1 2022-04-27 11:38:36  LS002           1.5            53          33          71  [29, 30, 45, 35, 69, 26, 34, 32, 48, 37, 55, 3...
+                 time     sn  cellsoc_diff cellvolt_diff cellmin_num cellmax_num                                      cellvolt_rank
+0 2022-04-28 11:47:36  LS002           1.5             5          21          50  [26, 39, 43, 37, 42, 34, 32, 31, 30, 29, 28, 4...
+                 time     sn  cellsoc_diff cellvolt_diff cellmin_num cellmax_num                                      cellvolt_rank
+0 2022-04-27 10:04:15  LS002           0.8            84          33          71  [39, 36, 42, 33, 69, 25, 31, 27, 46, 41, 51, 3...
+1 2022-04-27 11:38:36  LS002           1.5            53          33          71  [29, 30, 45, 35, 69, 26, 34, 32, 48, 37, 55, 3...

USER/SPF/alibaba/07BatSafetyWarning/电压离群.txt

@@ -0,0 +1,196 @@
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 16:00:00  LS001  [-2.32,0.44,0.54,0.73,0.54,-1.11,-0.98,-1.07,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+1 2022-04-26 17:00:00  LS001  [1.28,-0.76,-0.74,-0.79,-0.72,1.33,1.39,1.33,1...  [0.01,-0.01,-0.01,-0.01,-0.01,0.01,0.01,0.01,0...
+2 2022-04-26 18:00:00  LS001  [0.79,-0.86,-0.58,-0.82,-0.54,1.21,1.63,1.06,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-26 19:00:00  LS001  [0.46,-1.01,-0.51,-0.97,-0.49,1.4,1.7,1.19,1.5...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-26 20:00:00  LS001  [0.35,-0.94,-0.49,-0.88,-0.25,1.43,1.61,1.2,1....  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-26 21:00:00  LS001  [0.28,-0.89,-0.63,-0.75,0.06,1.39,1.25,1.15,1....  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 22:00:00  LS001  [0.53,-0.9,-0.62,-0.85,-0.38,1.41,1.54,1.19,1....  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+1 2022-04-26 23:00:00  LS001  [0.7,-0.87,-0.56,-0.88,-0.55,1.4,1.67,1.18,1.4...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+2 2022-04-27 00:00:00  LS001  [0.77,-0.86,-0.56,-0.89,-0.54,1.37,1.68,1.19,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 01:00:00  LS001  [0.83,-0.87,-0.61,-0.9,-0.5,1.36,1.66,1.2,1.49...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.01,0.0,0.0,0.0,...
+4 2022-04-27 02:00:00  LS001  [0.87,-0.93,-0.77,-0.89,-0.38,1.29,1.6,1.21,1....  [0.0,-0.0,-0.0,-0.0,-0.0,0.01,0.01,0.0,0.01,0....
+5 2022-04-27 03:00:00  LS001  [0.9,-0.94,-0.82,-0.85,-0.41,1.27,1.6,1.23,1.5...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 10:00:00  LS001  [-1.11,1.21,-0.19,1.32,0.19,-0.83,-2.21,-0.94,...  [-0.0,0.0,-0.0,0.0,0.0,-0.0,-0.01,-0.0,-0.0,-0...
+1 2022-04-27 11:00:00  LS001  [-1.4,-2.03,0.46,-1.31,0.19,0.13,0.84,0.47,1.0...  [-0.0,-0.0,0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+2 2022-04-27 12:00:00  LS001  [-1.12,0.78,0.43,1.26,0.56,-1.21,-0.84,-1.06,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+3 2022-04-27 13:00:00  LS001  [-2.0,-0.55,0.24,-0.33,-0.35,1.74,-0.83,1.58,-...  [-0.0,-0.0,0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0....
+4 2022-04-27 14:00:00  LS001  [0.12,-0.82,-0.43,-0.96,-0.65,1.57,1.52,1.17,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 15:00:00  LS001  [0.34,-0.94,-0.53,-0.93,-0.51,1.39,1.67,1.15,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 16:00:00  LS001  [0.29,-0.98,-0.68,-0.9,-0.36,1.37,1.74,1.2,1.6...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+1 2022-04-27 17:00:00  LS001  [0.33,-0.94,-1.03,-0.62,0.15,1.36,1.11,1.2,1.1...  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+2 2022-04-27 18:00:00  LS001  [0.42,-0.95,-0.71,-0.74,-0.15,1.31,1.46,1.22,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 19:00:00  LS001  [0.47,-0.85,-0.53,-0.84,-0.43,1.31,1.73,1.13,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-27 20:00:00  LS001  [0.49,-0.72,-0.45,-0.91,-0.52,1.35,1.79,1.05,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 21:00:00  LS001  [0.53,-0.65,-0.47,-0.99,-0.49,1.35,1.76,0.99,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 16:00:00  LS001  [-2.32,0.44,0.54,0.73,0.54,-1.11,-0.98,-1.07,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+1 2022-04-26 17:00:00  LS001  [1.28,-0.76,-0.74,-0.79,-0.72,1.33,1.39,1.33,1...  [0.01,-0.01,-0.01,-0.01,-0.01,0.01,0.01,0.01,0...
+2 2022-04-26 18:00:00  LS001  [0.79,-0.86,-0.58,-0.82,-0.54,1.21,1.63,1.06,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-26 19:00:00  LS001  [0.46,-1.01,-0.51,-0.97,-0.49,1.4,1.7,1.19,1.5...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-26 20:00:00  LS001  [0.35,-0.94,-0.49,-0.88,-0.25,1.43,1.61,1.2,1....  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-26 21:00:00  LS001  [0.28,-0.89,-0.63,-0.75,0.06,1.39,1.25,1.15,1....  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 22:00:00  LS001  [0.53,-0.9,-0.62,-0.85,-0.38,1.41,1.54,1.19,1....  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+1 2022-04-26 23:00:00  LS001  [0.7,-0.87,-0.56,-0.88,-0.55,1.4,1.67,1.18,1.4...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+2 2022-04-27 00:00:00  LS001  [0.77,-0.86,-0.56,-0.89,-0.54,1.37,1.68,1.19,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 01:00:00  LS001  [0.83,-0.87,-0.61,-0.9,-0.5,1.36,1.66,1.2,1.49...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.01,0.0,0.0,0.0,...
+4 2022-04-27 02:00:00  LS001  [0.87,-0.93,-0.77,-0.89,-0.38,1.29,1.6,1.21,1....  [0.0,-0.0,-0.0,-0.0,-0.0,0.01,0.01,0.0,0.01,0....
+5 2022-04-27 03:00:00  LS001  [0.9,-0.94,-0.82,-0.85,-0.41,1.27,1.6,1.23,1.5...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 16:00:00  LS001  [-2.32,0.44,0.54,0.73,0.54,-1.11,-0.98,-1.07,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+1 2022-04-26 17:00:00  LS001  [1.28,-0.76,-0.74,-0.79,-0.72,1.33,1.39,1.33,1...  [0.01,-0.01,-0.01,-0.01,-0.01,0.01,0.01,0.01,0...
+2 2022-04-26 18:00:00  LS001  [0.79,-0.86,-0.58,-0.82,-0.54,1.21,1.63,1.06,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-26 19:00:00  LS001  [0.46,-1.01,-0.51,-0.97,-0.49,1.4,1.7,1.19,1.5...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-26 20:00:00  LS001  [0.35,-0.94,-0.49,-0.88,-0.25,1.43,1.61,1.2,1....  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-26 21:00:00  LS001  [0.28,-0.89,-0.63,-0.75,0.06,1.39,1.25,1.15,1....  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 22:00:00  LS001  [0.53,-0.9,-0.62,-0.85,-0.38,1.41,1.54,1.19,1....  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+1 2022-04-26 23:00:00  LS001  [0.7,-0.87,-0.56,-0.88,-0.55,1.4,1.67,1.18,1.4...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+2 2022-04-27 00:00:00  LS001  [0.77,-0.86,-0.56,-0.89,-0.54,1.37,1.68,1.19,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 01:00:00  LS001  [0.83,-0.87,-0.61,-0.9,-0.5,1.36,1.66,1.2,1.49...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.01,0.0,0.0,0.0,...
+4 2022-04-27 02:00:00  LS001  [0.87,-0.93,-0.77,-0.89,-0.38,1.29,1.6,1.21,1....  [0.0,-0.0,-0.0,-0.0,-0.0,0.01,0.01,0.0,0.01,0....
+5 2022-04-27 03:00:00  LS001  [0.9,-0.94,-0.82,-0.85,-0.41,1.27,1.6,1.23,1.5...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 10:00:00  LS001  [-1.11,1.21,-0.19,1.32,0.19,-0.83,-2.21,-0.94,...  [-0.0,0.0,-0.0,0.0,0.0,-0.0,-0.01,-0.0,-0.0,-0...
+1 2022-04-27 11:00:00  LS001  [-1.4,-2.03,0.46,-1.31,0.19,0.13,0.84,0.47,1.0...  [-0.0,-0.0,0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+2 2022-04-27 12:00:00  LS001  [-1.12,0.78,0.43,1.26,0.56,-1.21,-0.84,-1.06,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+3 2022-04-27 13:00:00  LS001  [-2.0,-0.55,0.24,-0.33,-0.35,1.74,-0.83,1.58,-...  [-0.0,-0.0,0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0....
+4 2022-04-27 14:00:00  LS001  [0.12,-0.82,-0.43,-0.96,-0.65,1.57,1.52,1.17,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 15:00:00  LS001  [0.34,-0.94,-0.53,-0.93,-0.51,1.39,1.67,1.15,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 16:00:00  LS001  [0.29,-0.98,-0.68,-0.9,-0.36,1.37,1.74,1.2,1.6...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+1 2022-04-27 17:00:00  LS001  [0.33,-0.94,-1.03,-0.62,0.15,1.36,1.11,1.2,1.1...  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+2 2022-04-27 18:00:00  LS001  [0.42,-0.95,-0.71,-0.74,-0.15,1.31,1.46,1.22,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 19:00:00  LS001  [0.47,-0.85,-0.53,-0.84,-0.43,1.31,1.73,1.13,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-27 20:00:00  LS001  [0.49,-0.72,-0.45,-0.91,-0.52,1.35,1.79,1.05,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 21:00:00  LS001  [0.53,-0.65,-0.47,-0.99,-0.49,1.35,1.76,0.99,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 12:00:00  LS001  [-1.12,0.78,0.43,1.26,0.56,-1.21,-0.84,-1.06,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+1 2022-04-27 13:00:00  LS001  [-2.0,-0.55,0.24,-0.33,-0.35,1.74,-0.83,1.58,-...  [-0.0,-0.0,0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0....
+2 2022-04-27 14:00:00  LS001  [0.12,-0.82,-0.43,-0.96,-0.65,1.57,1.52,1.17,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 15:00:00  LS001  [0.34,-0.94,-0.53,-0.93,-0.51,1.39,1.67,1.15,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-27 16:00:00  LS001  [0.29,-0.98,-0.68,-0.9,-0.36,1.37,1.74,1.2,1.6...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 17:00:00  LS001  [0.33,-0.94,-1.03,-0.62,0.15,1.36,1.11,1.2,1.1...  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 12:00:00  LS001  [-1.12,0.78,0.43,1.26,0.56,-1.21,-0.84,-1.06,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+1 2022-04-27 13:00:00  LS001  [-2.0,-0.55,0.24,-0.33,-0.35,1.74,-0.83,1.58,-...  [-0.0,-0.0,0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0....
+2 2022-04-27 14:00:00  LS001  [0.12,-0.82,-0.43,-0.96,-0.65,1.57,1.52,1.17,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 15:00:00  LS001  [0.34,-0.94,-0.53,-0.93,-0.51,1.39,1.67,1.15,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-27 16:00:00  LS001  [0.29,-0.98,-0.68,-0.9,-0.36,1.37,1.74,1.2,1.6...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 17:00:00  LS001  [0.33,-0.94,-1.03,-0.62,0.15,1.36,1.11,1.2,1.1...  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-28 12:00:00  LS001  [-0.56,1.81,0.26,1.47,-0.51,-1.37,-2.17,-1.26,...  [-0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0....
+1 2022-04-28 13:00:00  LS001  [0.29,-0.03,-0.28,-0.23,-0.83,0.31,1.28,0.28,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+2 2022-04-28 14:00:00  LS001  [0.46,-0.66,-0.68,-0.71,-0.77,0.46,1.68,0.46,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-28 15:00:00  LS001  [0.43,-0.77,-0.69,-0.75,-0.79,0.43,1.65,0.43,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-28 16:00:00  LS001  [0.2,-0.55,-0.26,-0.41,-1.04,0.5,1.46,0.21,1.4...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-28 17:00:00  LS001  [-0.13,-0.19,-0.14,-0.16,-0.69,1.2,1.27,0.72,1...  [-0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 12:00:00  LS001  [-1.12,0.78,0.43,1.26,0.56,-1.21,-0.84,-1.06,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+1 2022-04-27 13:00:00  LS001  [-2.0,-0.55,0.24,-0.33,-0.35,1.74,-0.83,1.58,-...  [-0.0,-0.0,0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0....
+2 2022-04-27 14:00:00  LS001  [0.12,-0.82,-0.43,-0.96,-0.65,1.57,1.52,1.17,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 15:00:00  LS001  [0.34,-0.94,-0.53,-0.93,-0.51,1.39,1.67,1.15,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-27 16:00:00  LS001  [0.29,-0.98,-0.68,-0.9,-0.36,1.37,1.74,1.2,1.6...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 17:00:00  LS001  [0.33,-0.94,-1.03,-0.62,0.15,1.36,1.11,1.2,1.1...  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-28 12:00:00  LS001  [-0.56,1.81,0.26,1.47,-0.51,-1.37,-2.17,-1.26,...  [-0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0....
+1 2022-04-28 13:00:00  LS001  [0.29,-0.03,-0.28,-0.23,-0.83,0.31,1.28,0.28,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+2 2022-04-28 14:00:00  LS001  [0.46,-0.66,-0.68,-0.71,-0.77,0.46,1.68,0.46,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-28 15:00:00  LS001  [0.43,-0.77,-0.69,-0.75,-0.79,0.43,1.65,0.43,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-28 16:00:00  LS001  [0.2,-0.55,-0.26,-0.41,-1.04,0.5,1.46,0.21,1.4...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-28 17:00:00  LS001  [-0.13,-0.19,-0.14,-0.16,-0.69,1.2,1.27,0.72,1...  [-0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 12:00:00  LS001  [-1.12,0.78,0.43,1.26,0.56,-1.21,-0.84,-1.06,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+1 2022-04-27 13:00:00  LS001  [-2.0,-0.55,0.24,-0.33,-0.35,1.74,-0.83,1.58,-...  [-0.0,-0.0,0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0....
+2 2022-04-27 14:00:00  LS001  [0.12,-0.82,-0.43,-0.96,-0.65,1.57,1.52,1.17,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 15:00:00  LS001  [0.34,-0.94,-0.53,-0.93,-0.51,1.39,1.67,1.15,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-27 16:00:00  LS001  [0.29,-0.98,-0.68,-0.9,-0.36,1.37,1.74,1.2,1.6...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 17:00:00  LS001  [0.33,-0.94,-1.03,-0.62,0.15,1.36,1.11,1.2,1.1...  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-28 12:00:00  LS001  [-0.56,1.81,0.26,1.47,-0.51,-1.37,-2.17,-1.26,...  [-0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0....
+1 2022-04-28 13:00:00  LS001  [0.29,-0.03,-0.28,-0.23,-0.83,0.31,1.28,0.28,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+2 2022-04-28 14:00:00  LS001  [0.46,-0.66,-0.68,-0.71,-0.77,0.46,1.68,0.46,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-28 15:00:00  LS001  [0.43,-0.77,-0.69,-0.75,-0.79,0.43,1.65,0.43,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-28 16:00:00  LS001  [0.2,-0.55,-0.26,-0.41,-1.04,0.5,1.46,0.21,1.4...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-28 17:00:00  LS001  [-0.13,-0.19,-0.14,-0.16,-0.69,1.2,1.27,0.72,1...  [-0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 12:00:00  LS001  [-1.12,0.78,0.43,1.26,0.56,-1.21,-0.84,-1.06,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+1 2022-04-27 13:00:00  LS001  [-2.0,-0.55,0.24,-0.33,-0.35,1.74,-0.83,1.58,-...  [-0.0,-0.0,0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0....
+2 2022-04-27 14:00:00  LS001  [0.12,-0.82,-0.43,-0.96,-0.65,1.57,1.52,1.17,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 15:00:00  LS001  [0.34,-0.94,-0.53,-0.93,-0.51,1.39,1.67,1.15,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-27 16:00:00  LS001  [0.29,-0.98,-0.68,-0.9,-0.36,1.37,1.74,1.2,1.6...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 17:00:00  LS001  [0.33,-0.94,-1.03,-0.62,0.15,1.36,1.11,1.2,1.1...  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-28 12:00:00  LS001  [-0.56,1.81,0.26,1.47,-0.51,-1.37,-2.17,-1.26,...  [-0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0....
+1 2022-04-28 13:00:00  LS001  [0.29,-0.03,-0.28,-0.23,-0.83,0.31,1.28,0.28,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+2 2022-04-28 14:00:00  LS001  [0.46,-0.66,-0.68,-0.71,-0.77,0.46,1.68,0.46,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-28 15:00:00  LS001  [0.43,-0.77,-0.69,-0.75,-0.79,0.43,1.65,0.43,0...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-28 16:00:00  LS001  [0.2,-0.55,-0.26,-0.41,-1.04,0.5,1.46,0.21,1.4...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-28 17:00:00  LS001  [-0.13,-0.19,-0.14,-0.16,-0.69,1.2,1.27,0.72,1...  [-0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 12:00:00  LS001  [-1.12,0.78,0.43,1.26,0.56,-1.21,-0.84,-1.06,-...  [-0.0,0.0,0.0,0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0...
+1 2022-04-27 13:00:00  LS001  [-2.0,-0.55,0.24,-0.33,-0.35,1.74,-0.83,1.58,-...  [-0.0,-0.0,0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0....
+2 2022-04-27 14:00:00  LS001  [0.12,-0.82,-0.43,-0.96,-0.65,1.57,1.52,1.17,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+3 2022-04-27 15:00:00  LS001  [0.34,-0.94,-0.53,-0.93,-0.51,1.39,1.67,1.15,1...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+4 2022-04-27 16:00:00  LS001  [0.29,-0.98,-0.68,-0.9,-0.36,1.37,1.74,1.2,1.6...  [0.0,-0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0...
+5 2022-04-27 17:00:00  LS001  [0.33,-0.94,-1.03,-0.62,0.15,1.36,1.11,1.2,1.1...  [0.0,-0.0,-0.0,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 18:00:00  LS002  [1.05,-0.68,-0.13,-0.15,0.3,-0.69,0.03,-0.62,-...  [0.0,-0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0.0,0.0...
+1 2022-04-26 19:00:00  LS002  [1.03,-0.54,-0.21,-0.23,-0.47,-0.64,0.01,-0.73...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+2 2022-04-26 20:00:00  LS002  [1.05,-0.47,-0.16,-0.17,-0.5,-0.58,0.06,-0.72,...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+3 2022-04-26 21:00:00  LS002  [1.05,-0.54,0.16,-0.31,-0.48,-0.51,0.24,-0.64,...  [0.0,-0.0,0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0....
+4 2022-04-26 22:00:00  LS002  [1.08,-0.48,-0.03,-0.19,-0.59,-0.55,0.12,-0.65...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+5 2022-04-26 23:00:00  LS002  [1.18,-0.35,-0.18,-0.08,-0.66,-0.56,0.06,-0.67...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 12:00:00  LS002  [-1.6,0.47,0.03,0.19,0.52,0.4,-0.63,0.74,-0.39...  [-0.0,0.0,0.0,0.0,0.0,0.0,-0.0,0.0,-0.0,-0.0,-...
+1 2022-04-27 13:00:00  LS002  [0.94,-0.65,-0.96,-0.81,1.46,-1.07,-1.76,-1.18...  [0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0...
+2 2022-04-27 14:00:00  LS002  [0.65,-0.6,-0.07,-0.5,-0.34,-0.64,0.0,-0.79,-0...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+3 2022-04-27 15:00:00  LS002  [0.89,-0.44,-0.28,-0.13,-0.33,-0.74,-0.02,-0.8...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0...
+4 2022-04-27 16:00:00  LS002  [0.88,-0.36,-0.34,0.01,-0.29,-0.65,-0.05,-0.89...  [0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0....
+5 2022-04-27 17:00:00  LS002  [1.04,-0.53,0.14,-0.21,-0.37,-0.5,0.18,-0.66,-...  [0.0,-0.0,0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-28 12:00:00  LS002  [-1.16,-0.77,0.58,-0.44,0.79,-0.38,-0.1,-0.21,...  [-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0...
+1 2022-04-28 13:00:00  LS002  [-1.92,0.0,0.28,0.16,0.61,0.42,-0.35,0.77,-0.1...  [-0.0,0.0,0.0,0.0,0.0,0.0,-0.0,0.0,-0.0,-0.0,-...
+2 2022-04-28 14:00:00  LS002  [0.87,-0.38,0.42,-0.34,-0.38,0.14,0.38,-0.08,0...  [0.0,-0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,0.0,0.0,0...
+3 2022-04-28 15:00:00  LS002  [-0.09,-0.86,-0.98,-0.79,2.27,-0.87,-1.24,-1.2...  [-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-...
+4 2022-04-28 16:00:00  LS002  [-0.46,-0.83,-0.85,-0.61,2.22,-0.67,-0.73,-1.0...  [-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-...
+5 2022-04-28 17:00:00  LS002  [-0.3,-0.61,-0.66,-0.3,2.08,-0.31,-0.3,-1.44,-...  [-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 18:00:00  LS002  [1.05,-0.68,-0.13,-0.15,0.3,-0.69,0.03,-0.62,-...  [0.0,-0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0.0,0.0...
+1 2022-04-26 19:00:00  LS002  [1.03,-0.54,-0.21,-0.23,-0.47,-0.64,0.01,-0.73...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+2 2022-04-26 20:00:00  LS002  [1.05,-0.47,-0.16,-0.17,-0.5,-0.58,0.06,-0.72,...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+3 2022-04-26 21:00:00  LS002  [1.05,-0.54,0.16,-0.31,-0.48,-0.51,0.24,-0.64,...  [0.0,-0.0,0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0....
+4 2022-04-26 22:00:00  LS002  [1.08,-0.48,-0.03,-0.19,-0.59,-0.55,0.12,-0.65...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+5 2022-04-26 23:00:00  LS002  [1.18,-0.35,-0.18,-0.08,-0.66,-0.56,0.06,-0.67...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-27 12:00:00  LS002  [-1.6,0.47,0.03,0.19,0.52,0.4,-0.63,0.74,-0.39...  [-0.0,0.0,0.0,0.0,0.0,0.0,-0.0,0.0,-0.0,-0.0,-...
+1 2022-04-27 13:00:00  LS002  [0.94,-0.65,-0.96,-0.81,1.46,-1.07,-1.76,-1.18...  [0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0...
+2 2022-04-27 14:00:00  LS002  [0.65,-0.6,-0.07,-0.5,-0.34,-0.64,0.0,-0.79,-0...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+3 2022-04-27 15:00:00  LS002  [0.89,-0.44,-0.28,-0.13,-0.33,-0.74,-0.02,-0.8...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0...
+4 2022-04-27 16:00:00  LS002  [0.88,-0.36,-0.34,0.01,-0.29,-0.65,-0.05,-0.89...  [0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0....
+5 2022-04-27 17:00:00  LS002  [1.04,-0.53,0.14,-0.21,-0.37,-0.5,0.18,-0.66,-...  [0.0,-0.0,0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-28 12:00:00  LS002  [-1.16,-0.77,0.58,-0.44,0.79,-0.38,-0.1,-0.21,...  [-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-0...
+1 2022-04-28 13:00:00  LS002  [-1.92,0.0,0.28,0.16,0.61,0.42,-0.35,0.77,-0.1...  [-0.0,0.0,0.0,0.0,0.0,0.0,-0.0,0.0,-0.0,-0.0,-...
+2 2022-04-28 14:00:00  LS002  [0.87,-0.38,0.42,-0.34,-0.38,0.14,0.38,-0.08,0...  [0.0,-0.0,0.0,-0.0,-0.0,0.0,0.0,-0.0,0.0,0.0,0...
+3 2022-04-28 15:00:00  LS002  [-0.09,-0.86,-0.98,-0.79,2.27,-0.87,-1.24,-1.2...  [-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-...
+4 2022-04-28 16:00:00  LS002  [-0.46,-0.83,-0.85,-0.61,2.22,-0.67,-0.73,-1.0...  [-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-...
+5 2022-04-28 17:00:00  LS002  [-0.3,-0.61,-0.66,-0.3,2.08,-0.31,-0.3,-1.44,-...  [-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0.0,-0.0,-...
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 18:00:00  LS002  [1.05,-0.68,-0.13,-0.15,0.3,-0.69,0.03,-0.62,-...  [0.0,-0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0.0,0.0...
+1 2022-04-26 19:00:00  LS002  [1.03,-0.54,-0.21,-0.23,-0.47,-0.64,0.01,-0.73...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+2 2022-04-26 20:00:00  LS002  [1.05,-0.47,-0.16,-0.17,-0.5,-0.58,0.06,-0.72,...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+3 2022-04-26 21:00:00  LS002  [1.05,-0.54,0.16,-0.31,-0.48,-0.51,0.24,-0.64,...  [0.0,-0.0,0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0....
+4 2022-04-26 22:00:00  LS002  [1.08,-0.48,-0.03,-0.19,-0.59,-0.55,0.12,-0.65...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+5 2022-04-26 23:00:00  LS002  [1.18,-0.35,-0.18,-0.08,-0.66,-0.56,0.06,-0.67...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+                 time     sn                                          VolOl_Uni                                        VolChng_Uni
+0 2022-04-26 18:00:00  LS002  [1.05,-0.68,-0.13,-0.15,0.3,-0.69,0.03,-0.62,-...  [0.0,-0.0,-0.0,-0.0,0.0,-0.0,0.0,-0.0,-0.0,0.0...
+1 2022-04-26 19:00:00  LS002  [1.03,-0.54,-0.21,-0.23,-0.47,-0.64,0.01,-0.73...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+2 2022-04-26 20:00:00  LS002  [1.05,-0.47,-0.16,-0.17,-0.5,-0.58,0.06,-0.72,...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+3 2022-04-26 21:00:00  LS002  [1.05,-0.54,0.16,-0.31,-0.48,-0.51,0.24,-0.64,...  [0.0,-0.0,0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,-0....
+4 2022-04-26 22:00:00  LS002  [1.08,-0.48,-0.03,-0.19,-0.59,-0.55,0.12,-0.65...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....
+5 2022-04-26 23:00:00  LS002  [1.18,-0.35,-0.18,-0.08,-0.66,-0.56,0.06,-0.67...  [0.0,-0.0,-0.0,-0.0,-0.0,-0.0,0.0,-0.0,-0.0,0....

USER/SPF/alibaba/08BatCrtl/CBMSBatCtrl.py

@@ -6,31 +6,28 @@ import json
 from USER.SPF.alibaba.Common import BatParam
 
 class BatCrtl():
-    def __init__(self,sn,celltype,host,port,df_statcrtl_ram,df_rlycrtl_ram,df_diag,df_bms1,df_bms2,df_bms3):  #参数初始化
+    def __init__(self,host,port,df_statectrl_ram,df_rlyctrl_ram,df_diag,df_bms,sn_list):  #参数初始化
 
-        self.sn=sn
-        self.celltype=celltype
+        celltype=1
         self.host=host
         self.port=port
+        self.sn_list=sn_list
         self.param=BatParam.BatParam(celltype)
         
-        df_bms1['time']= pd.to_datetime(df_bms1['time'], format='%Y-%m-%d %H:%M:%S')
-        df_bms2['time']= pd.to_datetime(df_bms2['time'], format='%Y-%m-%d %H:%M:%S')
-        df_bms3['time']= pd.to_datetime(df_bms2['time'], format='%Y-%m-%d %H:%M:%S')
-        self.df_bms1=df_bms1
-        self.df_bms2=df_bms2
-        self.df_bms3=df_bms3
-        self.df_statcrtl_ram=df_statcrtl_ram.copy()
-        self.df_rlycrtl_ram=df_rlycrtl_ram.copy()
-        self.df_diag=df_diag[(df_diag['fault_code']==110) | (df_diag['fault_code']==119)]
+        df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        self.df_bms=df_bms
+        self.df_statectrl_ram=df_statectrl_ram.copy()
+        self.df_rlyctrl_ram=df_rlyctrl_ram.copy()
+        self.df_diag1=df_diag[(df_diag['fault_code']==110) | (df_diag['fault_code']==119) | (df_diag['fault_code']==12) | (df_diag['fault_code']==18)]
+        self.df_diag2=df_diag[df_diag['fault_code']==100]
         
         # self.bmstime=df_bms['time']
         # self.packcrnt=df_bms['PackCrnt']*self.param.PackCrntDec
         # self.bms_soc=df_bms['PackSOC']
         # self.bms_soh=df_bms['PackSOH']
         # self.bmsstat=df_bms['BMSStat']
-        # self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
-        # self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
+        self.cellvolt_name=['CellVolt'+str(x) for x in range(1,self.param.CellVoltNums+1)]
+        self.celltemp_name=['CellTemp'+str(x) for x in range(1,self.param.CellTempNums+1)]
 
     #主调用函数........................................................................
     def bat_crtl(self):
@@ -53,41 +50,177 @@ class BatCrtl():
     #电池继电器下行控制.........................................................................................................................
     def _bat_rlycrtl(self):
         time_now=datetime.datetime.now()
-        for i in range(1,4):
-            sn='KPD00'+str(i)
-            if sn in list(self.df_diag['sn']):  #该sn发生4级或5级故障
-                if (sn in list(self.df_rlycrtl_ram['sn'])): #ram是否包含该sn
-                    if self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'batrlycrtl']==0:
-                        self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'time']=time_now
-                        self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'batrlycrtl']=1
-                        dict_send={"cmd_type":1,"sn":sn,"batrlycrtl":1}
+        if self.df_bms.empty:
+            factory='康普盾'
+        else:
+            factory=self.df_bms.iloc[0]['factory'] 
+        for sn in self.sn_list:
+            if sn in list(self.df_diag1['sn']):  #该sn发生4级或5级故障
+                if sn in list(self.df_rlyctrl_ram['sn']): #ram是否包含该sn
+                    if self.df_rlyctrl_ram.loc[self.df_rlyctrl_ram[self.df_rlyctrl_ram['sn']==sn].index[0], 'batrlyctrl']==0:
+                        self.df_rlyctrl_ram.loc[self.df_rlyctrl_ram[self.df_rlyctrl_ram['sn']==sn].index[0], 'time']=time_now
+                        self.df_rlyctrl_ram.loc[self.df_rlyctrl_ram[self.df_rlyctrl_ram['sn']==sn].index[0], 'batrlyctrl']=1
+                        dict_send={"cmd_type":1,"factory":factory,"sn":sn,"batrlyctrl":1}
                         self._rabbit_send(dict_send)
-                        return self.df_rlycrtl_ram
+                        return self.df_rlyctrl_ram
                     else:
                         pass
                 else:
-                    self.df_rlycrtl_ram[len(self.df_rlycrtl_ram)]=[time_now,sn,1]
-                    dict_send={"cmd_type":1,"sn":sn,"batrlycrtl":1}
+                    self.df_rlyctrl_ram.loc[len(self.df_rlyctrl_ram)]=[time_now,sn,1]
+                    dict_send={"cmd_type":1,"factory":factory,"sn":sn,"batrlyctrl":1}
                     self._rabbit_send(dict_send)
-                    return self.df_rlycrtl_ram
+                    return self.df_rlyctrl_ram
             else:   #该sn没有4级和5级故障
-                if (sn in list(self.df_rlycrtl_ram['sn'])): #ram是否包含该sn
-                    if self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'batrlycrtl']==1:
-                        self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'time']=time_now
-                        self.df_rlycrtl_ram.loc[self.df_rlycrtl_ram[self.df_rlycrtl_ram['sn']==sn].index[0], 'batrlycrtl']=0
-                        dict_send={"cmd_type":1,"sn":sn,"batrlycrtl":0}
+                if sn in list(self.df_rlyctrl_ram['sn']): #ram是否包含该sn
+                    if self.df_rlyctrl_ram.loc[self.df_rlyctrl_ram[self.df_rlyctrl_ram['sn']==sn].index[0], 'batrlyctrl']==1:
+                        self.df_rlyctrl_ram.loc[self.df_rlyctrl_ram[self.df_rlyctrl_ram['sn']==sn].index[0], 'time']=time_now
+                        self.df_rlyctrl_ram.loc[self.df_rlyctrl_ram[self.df_rlyctrl_ram['sn']==sn].index[0], 'batrlyctrl']=0
+                        dict_send={"cmd_type":1,"factory":factory,"sn":sn,"batrlyctrl":0}
                         self._rabbit_send(dict_send)
-                        return self.df_rlycrtl_ram
+                        return self.df_rlyctrl_ram
                     else:
                         pass
                 else:
-                    self.df_rlycrtl_ram[len(self.df_rlycrtl_ram)]=[time_now,sn,0]
-                    dict_send={"cmd_type":1,"sn":sn,"batrlycrtl":0}
+                    self.df_rlyctrl_ram.loc[len(self.df_rlyctrl_ram)]=[time_now,sn,0]
+                    dict_send={"cmd_type":1,"factory":factory,"sn":sn,"batrlyctrl":0}
                     self._rabbit_send(dict_send)
-                    return self.df_rlycrtl_ram
-        return self.df_rlycrtl_ram
+                    return self.df_rlyctrl_ram
+        return self.df_rlyctrl_ram
 
-    #电池继电器下行控制.........................................................................................................................
+    #电池状态下行控制.........................................................................................................................
     def _bat_statecrtl(self):
         time_now=datetime.datetime.now()
+        if self.df_bms.empty:
+            factory='康普盾'
+        else:
+            factory=self.df_bms.iloc[0]['factory']
+        #对完成充放电循环，且超过8天的，done_flg置0
+        if not self.df_statectrl_ram.empty:
+            for i in range(len(self.df_statectrl_ram)):
+                if self.df_statectrl_ram.iloc[i]['done_flg']==1 and (time_now-self.df_statectrl_ram.iloc[i]['time']).total_seconds()>10*24*3600:
+                    self.df_statectrl_ram.iloc[i]['done_flg']=0
+                else:
+                    pass
+        else:
+            pass
+        
+        #存在电池正在放电-------------------------------
+        if 3 in list(self.df_statectrl_ram['batstatectrl']):    
+            sn=self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['batstatectrl']==3].index[0], 'sn']
+            bms_index=self.df_bms[self.df_bms['sn']==sn].index[0]
+            packsoc=self.df_bms.loc[bms_index,'PackSOC']
+            cellvolt = self.df_bms.loc[bms_index,self.cellvolt_name]
+            if packsoc<10 or min(cellvolt)<3:   #结束放电
+                self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'time']=time_now
+                self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'batstatectrl']=2
+                dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":2,"dischrg_volt":272}
+                self._rabbit_send(dict_send)
+                return self.df_statectrl_ram
+            else:
+                pass
+        #存在电池正在静置---------------------------------
+        elif 2 in list(self.df_statectrl_ram['batstatectrl']):    
+            sn=self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['batstatectrl']==2].index[0], 'sn']
+            bms_index=self.df_bms[self.df_bms['sn']==sn].index[0]
+            packcrnt=self.df_bms.loc[bms_index,'PackCrnt']
+            packsoc=self.df_bms.loc[bms_index,'PackSOC']
+            cellvolt = self.df_bms.loc[bms_index,self.cellvolt_name]
+            if packsoc>25 or min(cellvolt)>3.5:
+                self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'time']=time_now
+                self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'batstatectrl']=3
+                dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":3,"dischrg_volt":272}
+                self._rabbit_send(dict_send)
+                return self.df_statectrl_ram
+            elif abs(packcrnt)<0.1:   #当前工况确实在静置
+                time_st=self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'time']
+                if (time_now-time_st).total_seconds()>3700: #静置时间满足要求
+                    self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'time']=time_now
+                    self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'batstatectrl']=1
+                    dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":1,"dischrg_volt":272}
+                    self._rabbit_send(dict_send)
+                    return self.df_statectrl_ram
+                else:
+                    pass
+            else:
+                self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'time']=time_now
+                self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'batstatectrl']=2
+                dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":2,"dischrg_volt":272}
+                self._rabbit_send(dict_send)
+                return self.df_statectrl_ram
+        #存在电池正在充电------------------------------------
+        elif 1 in list(self.df_statectrl_ram['batstatectrl']):    
+            sn=self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['batstatectrl']==1].index[0], 'sn']
+            bms_index=self.df_bms[self.df_bms['sn']==sn].index[0]
+            packsoc=self.df_bms.loc[bms_index,'PackSOC']
+            cellvolt = self.df_bms.loc[bms_index,self.cellvolt_name]
+            if packsoc>99 or max(cellvolt)>self.param.CellFullChrgVolt: #充电已完成
+                self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'time']=time_now
+                self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'batstatectrl']=0
+                self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'done_flg']=1
+                dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":0,"dischrg_volt":270}
+                self._rabbit_send(dict_send)
+                return self.df_statectrl_ram
+            else:
+                pass
+        #当前电池均不在充放电循环过程中，则接下来判断是否存在100故障---------------------------------
+        else:   
+            for sn in self.sn_list:
+                if sn in list(self.df_diag2['sn']):  #该sn发生100长时间未标定故障
+                    if sn in list(self.df_bms['sn']):
+                        bms_index=self.df_bms[self.df_bms['sn']==sn].index[0]
+                        packsoc=self.df_bms.loc[bms_index,'PackSOC']
+                        cellvolt = self.df_bms.loc[bms_index,self.cellvolt_name]
+                        if packsoc>20 or min(cellvolt)>3.5:
+                            if sn in list(self.df_statectrl_ram['sn']): #该sn的ram不为空
+                                if self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'done_flg']==0:
+                                    self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'time']=time_now
+                                    self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'batstatectrl']=3
+                                    dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":3,"dischrg_volt":272}
+                                    self._rabbit_send(dict_send)
+                                    return self.df_statectrl_ram
+                                else:
+                                    pass
+                            else:
+                                self.df_statectrl_ram.loc[len(self.df_rlyctrl_ram)]=[time_now,sn,3,0]
+                                dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":3,"dischrg_volt":272}
+                                self._rabbit_send(dict_send)
+                                return self.df_statectrl_ram
+                        else:
+                            if sn in list(self.df_statectrl_ram['sn']): #该sn的ram不为空
+                                if self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'done_flg']==0:
+                                    self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'time']=time_now
+                                    self.df_statectrl_ram.loc[self.df_statectrl_ram[self.df_statectrl_ram['sn']==sn].index[0], 'batstatectrl']=2
+                                    dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":2,"dischrg_volt":272}
+                                    self._rabbit_send(dict_send)
+                                    return self.df_statectrl_ram
+                                else:
+                                    pass
+                            else:
+                                self.df_statectrl_ram.loc[len(self.df_rlyctrl_ram)]=[time_now,sn,2,0]
+                                dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":2,"dischrg_volt":272}
+                                self._rabbit_send(dict_send)
+                                return self.df_statectrl_ram
+                    else:   #原始数据库为空，给寄存器赋0
+                        if sn in list(self.df_statectrl_ram['sn']):
+                            pass
+                        else:
+                            self.df_statectrl_ram.loc[len(self.df_rlyctrl_ram)]=[time_now,sn,0,0]
+                            dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":0,"dischrg_volt":272}
+                            self._rabbit_send(dict_send)
+                            return self.df_statectrl_ram
+                else:   #没有相关故障，给寄存器赋0
+                    if sn in list(self.df_statectrl_ram['sn']):
+                        pass
+                    else:
+                        self.df_statectrl_ram.loc[len(self.df_rlyctrl_ram)]=[time_now,sn,0,0]
+                        dict_send={"cmd_type":2,"factory":factory,"sn":sn,"batstatectrl":0,"dischrg_volt":272}
+                        self._rabbit_send(dict_send)
+                        return self.df_statectrl_ram
+        
+        return self.df_statectrl_ram
+
 
+
+
+        
+                

USER/SPF/alibaba/08BatCrtl/main.py

@@ -1,64 +1,83 @@
-import datetime
+import numpy as np
 import pandas as pd
 import datetime
-from USER.SPF.alibaba.Common import DBDownload
+import pymysql
+from apscheduler.schedulers.blocking import BlockingScheduler
 from USER.SPF.alibaba.Common import log
-import CBMSBatSoh
+import CBMSBatCtrl
 
-if __name__ == "__main__":
-    SNnums= ['LISHEN01']
+#...................................SOC计算函数......................................................................................................................
+def ctrl_cal():
+    global df_ram_rlyctrl
+    global df_ram_statectrl
+    global now_time
+    global df_bms
+
+    end_time=now_time+datetime.timedelta(seconds=3)
+    start_time=now_time
+    now_time=now_time+datetime.timedelta(seconds=3)
 
-    end_time=datetime.datetime.now()-datetime.timedelta(seconds=10)
-    start_time=end_time-datetime.timedelta(seconds=12000)
-    start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
-    end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+    host='120.25.223.1'
+    port=4901
+    user='root'
+    password='123456'
+    db='ali'
+    mysql = pymysql.connect (host=host, user=user, password=password, port=port, database=db)
 
     #log信息配置............................................................................................................................................................................
-    mylog=log.Mylog('log_soh.txt','error')
+    mylog=log.Mylog('log_ctrl.txt','error')
     mylog.logcfg()
 
-    for sn in SNnums:
-        try:
-            # if 'PK500' in sn:
-            #     celltype=1 #6040三元电芯
-            # elif 'PK502' in sn:
-            #     celltype=2 #4840三元电芯
-            # elif 'PK504' in sn:
-            #     celltype=99    #60ah林磷酸铁锂电芯
-            # elif 'MGMLXN750' in sn:
-            #     celltype=3 #力信50ah三元电芯
-            # elif 'MGMCLN750' in sn: 
-            #     celltype=4 #CATL 50ah三元电芯
-            # else:
-            #     print('未找到对应电池编号！！！\n',sn)
-            #     # sys.exit()
+    sn_list=['KPD001','KPD002','KPD003']
+
+    try:
+        #读取所有康普盾电池包原始数据库最后一条数据...........................................................................
+ 
+        df_bms= df_bms[(df_bms['time']>=start_time) & (df_bms['time']<end_time)]
+        df_bms=df_bms.reset_index(drop=True)
+        cellvolt_name=['CellVolt'+str(x) for x in range(1,121)]
+        df_volt=pd.DataFrame([x[0].split(",") for x in np.array(df_bms[['cell_volt']])]).iloc[:,list(range(120))]
+        df_volt.columns = cellvolt_name
+        df_volt=df_volt.astype('float')
+        df_bms=pd.concat([df_bms,df_volt],axis=1)
+        df_bms.dropna(axis=0, inplace=True)
+
+
+        #读取所有未结束故障......................................................
+        df_diag=pd.DataFrame({'sn':['KPD001','KPD002','KPD003'],'fault_code':[100,100,100]})
+
+        #下行控制...................................................................................
+        host1='120.25.223.1'
+        port1=4902
+        BatCrtl=CBMSBatCtrl.BatCrtl(host1,port1,df_ram_statectrl,df_ram_rlyctrl,df_diag,df_bms,sn_list)
+        df_ram_rlyctrl, df_ram_statectrl=BatCrtl.bat_crtl()
+        # print('done！！！')
+            
+    except Exception as e:
+        print(repr(e))
+        mylog.logopt(e)
+        pass
+#...............................................主函数.......................................................................................................................
+if __name__ == "__main__":
+
+    #log信息配置
+    mylog=log.Mylog('log_ctrl.txt','error')
+    mylog.logcfg()
 
-            celltype=99
+    #参数初始化.........................................................................................................................................................................
+    df_ram_rlyctrl=pd.DataFrame(columns=['time','sn','batrlyctrl'])
+    df_ram_statectrl=pd.DataFrame(columns=['time','sn','batstatectrl','done_flg'])
 
-            #............................................................读取原始数据库数据...........................................................................
-            df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\力神40Ah-SOH原始数据.xlsx')
+    df_bms= pd.read_excel(r'D:\01WorkSpace\项目\Alibaba\data\原始数据_2022-01-10_2022-02-10\data(1)(1).xlsx')
+    now_time=datetime.datetime.strptime('2022-4-27 20:28:01','%Y-%m-%d %H:%M:%S')
 
-            #............................................................读取结果数据库数据.........................................................................
-            host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
-            port=3306
-            db='qx_cas'
-            user='qx_read'
-            password='Qx@123456'
-            mode=1
-            tablename='cellstateestimation_soh'
-            DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
-            with DBRead as DBRead:
-                df_soh=DBRead.getdata(param='time_st,time_sp,sn,method,soh,cellsoh,packsoh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
-            df_soh=pd.DataFrame()
-            # print(df_soh)
+    scheduler = BlockingScheduler()
+    scheduler.add_job(ctrl_cal, 'interval', seconds=0.05, id='crtl_job')
+    
+    try:
+        scheduler.start()
 
-            #.................................................................计算soh...................................................................................
-            BatSoh=CBMSBatSoh.BatSoh(sn,celltype,df_bms,df_soh)
-            df_res=BatSoh.batsoh()
-            df_res.to_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\99Result\\'+'CBMS_SOH_'+sn+'.xlsx')
-            # print('done！！！')
-             
-        except Exception as e:
-            print(repr(e))
-            mylog.logopt(sn,e)
-            pass
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)

USER/SPF/alibaba/101Log/log_ctrl.txt

@@ -0,0 +1,27 @@
+2022-04-27 21:10:03,244 - ERROR - (KeyError('factory'),)
+2022-04-27 21:10:03,629 - ERROR - Traceback (most recent call last):
+  File "C:\Users\NING MEI\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\indexes\base.py", line 3361, in get_loc
+    return self._engine.get_loc(casted_key)
+  File "pandas\_libs\index.pyx", line 76, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
+  File "pandas\_libs\hashtable_class_helper.pxi", line 5198, in pandas._libs.hashtable.PyObjectHashTable.get_item
+  File "pandas\_libs\hashtable_class_helper.pxi", line 5206, in pandas._libs.hashtable.PyObjectHashTable.get_item
+KeyError: 'factory'
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\08BatCrtl\main.py", line 53, in ctrl_cal
+    df_ram_rlyctrl, df_ram_statectrl=BatCrtl.bat_crtl()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\08BatCrtl\CBMSBatCtrl.py", line 34, in bat_crtl
+    df_res1=self._bat_rlycrtl()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\08BatCrtl\CBMSBatCtrl.py", line 56, in _bat_rlycrtl
+    factory=self.df_bms.iloc[0]['factory']
+  File "C:\Users\NING MEI\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\series.py", line 942, in __getitem__
+    return self._get_value(key)
+  File "C:\Users\NING MEI\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\series.py", line 1051, in _get_value
+    loc = self.index.get_loc(label)
+  File "C:\Users\NING MEI\AppData\Local\Programs\Python\Python38-32\lib\site-packages\pandas\core\indexes\base.py", line 3363, in get_loc
+    raise KeyError(key) from err
+KeyError: 'factory'
+

USER/SPF/alibaba/101Log/log_soc.txt

@@ -0,0 +1,50 @@
+2022-04-27 19:29:11,609 - ERROR - Job "soc_cal (trigger: interval[0:00:00.050000], next run at: 2022-04-27 19:29:11 CST)" raised an exception
+Traceback (most recent call last):
+  File "C:\Users\NING MEI\AppData\Local\Programs\Python\Python38-32\lib\site-packages\apscheduler\executors\base.py", line 125, in run_job
+    retval = job.func(*job.args, **job.kwargs)
+  File "d:/01WorkSpace/python/data_analyze_platform/USER/SPF/alibaba/04BatSoc/main.py", line 87, in soc_cal
+    df_res, df_ram_sn=BatSoc.batsoc()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\CBMSBatSoc.py", line 40, in batsoc
+    df_res, df_ram=self._lfp_soc()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\CBMSBatSoc.py", line 322, in _lfp_soc
+    ocvweight=eval(format(ocvweight,'.2f'))
+  File "<string>", line 1, in <module>
+NameError: name 'nan' is not defined
+2022-04-27 19:41:38,387 - ERROR - Job "soc_cal (trigger: interval[0:00:00.050000], next run at: 2022-04-27 19:41:38 CST)" raised an exception
+Traceback (most recent call last):
+  File "C:\Users\NING MEI\AppData\Local\Programs\Python\Python38-32\lib\site-packages\apscheduler\executors\base.py", line 125, in run_job
+    retval = job.func(*job.args, **job.kwargs)
+  File "d:/01WorkSpace/python/data_analyze_platform/USER/SPF/alibaba/04BatSoc/main.py", line 87, in soc_cal
+    df_res, df_ram_sn=BatSoc.batsoc()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\CBMSBatSoc.py", line 40, in batsoc
+    df_res, df_ram=self._lfp_soc()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\CBMSBatSoc.py", line 322, in _lfp_soc
+    ocvweight=eval(format(ocvweight,'.2f'))
+  File "<string>", line 1, in <module>
+NameError: name 'nan' is not defined
+2022-04-28 13:01:10,325 - ERROR - Job "soc_cal (trigger: interval[0:00:00.050000], next run at: 2022-04-28 13:01:10 CST)" raised an exception
+Traceback (most recent call last):
+  File "C:\Users\NING MEI\AppData\Local\Programs\Python\Python38-32\lib\site-packages\apscheduler\executors\base.py", line 125, in run_job
+    retval = job.func(*job.args, **job.kwargs)
+  File "d:/01WorkSpace/python/data_analyze_platform/USER/SPF/alibaba/04BatSoc/main.py", line 87, in soc_cal
+    df_res, df_ram_sn=BatSoc.batsoc()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\CBMSBatSoc.py", line 40, in batsoc
+    df_res, df_ram=self._lfp_soc()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\CBMSBatSoc.py", line 322, in _lfp_soc
+    ocvweight=eval(format(ocvweight,'.2f'))
+  File "<string>", line 1, in <module>
+NameError: name 'nan' is not defined
+2022-04-28 14:21:40,976 - ERROR - Job "soc_cal (trigger: interval[0:00:00.050000], next run at: 2022-04-28 14:21:41 CST)" raised an exception
+Traceback (most recent call last):
+  File "C:\Users\NING MEI\AppData\Local\Programs\Python\Python38-32\lib\site-packages\apscheduler\executors\base.py", line 125, in run_job
+    retval = job.func(*job.args, **job.kwargs)
+  File "d:/01WorkSpace/python/data_analyze_platform/USER/SPF/alibaba/04BatSoc/main.py", line 87, in soc_cal
+    df_res, df_ram_sn=BatSoc.batsoc()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\CBMSBatSoc.py", line 40, in batsoc
+    df_res, df_ram=self._lfp_soc()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\CBMSBatSoc.py", line 536, in _lfp_soc
+    cellsoc1=list(map(lambda x:eval(format(x,'.1f')), cellsoc))
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\04BatSoc\CBMSBatSoc.py", line 536, in <lambda>
+    cellsoc1=list(map(lambda x:eval(format(x,'.1f')), cellsoc))
+  File "<string>", line 1, in <module>
+NameError: name 'nan' is not defined

USER/SPF/alibaba/101Log/log_soh.txt

@@ -0,0 +1,10 @@
+2022-04-28 21:54:09,463 - ERROR - ('LS001', UnboundLocalError("local variable 'cellocv_st' referenced before assignment"))
+2022-04-28 21:54:09,464 - ERROR - Traceback (most recent call last):
+  File "d:/01WorkSpace/python/data_analyze_platform/USER/SPF/alibaba/01BatSoh/main.py", line 69, in <module>
+    df_res=BatSoh.batsoh()
+  File "d:\01WorkSpace\python\data_analyze_platform\USER\SPF\alibaba\01BatSoh\CBMSBatSoh.py", line 32, in batsoh
+    df_res=self._lfpsoh()
+  File "d:\01WorkSpace\pytoh\CBMSBatSoh.py", line 702, in _lfphon\data_analyze_platform\USER\SPF\alibaba\01BatSsoh
+    if not cellocv_st.empty:
+UnboundLocalError: local variable 'cellocv_st' referenced before assignment
+

USER/SPF/alibaba/Common/BatParam.py

@@ -133,13 +133,13 @@ class BatParam:
             self.TrwVoltRate=-1
 
         elif celltype==99:   #永联+力神
-            self.Capacity = 40*2
+            self.Capacity = 40
             self.CellVoltNums=72
             self.CellTempNums=32
             self.CellFullChrgVolt=3.5
             self.CellFullChrgCrnt=-self.Capacity/2
 
-            self.OcvInflexionBelow=3.285
+            self.OcvInflexionBelow=3.281
             self.OcvInflexion2=3.303
             self.OcvInflexion3=3.343
             self.OcvInflexionAbove=3.36
@@ -147,10 +147,10 @@ class BatParam:
             self.SocInflexion2=60
             self.SocInflexion3=70
 
-            self.FullChrgSoc=97
+            self.FullChrgSoc=100
             self.PeakSoc=62.5
-            self.PeakVoltLowLmt=3.37
-            self.PeakVoltUpLmt=3.42
+            self.PeakVoltLowLmt=3.35
+            self.PeakVoltUpLmt=3.4
             self.PeakCellVolt=[3.384,3.385,3.386,3.387,3.388]
             self.PackCrntDec=1
             self.BalCurrent=0.015

USER/SPF/alibaba/原始备份/04BatSoc/04BatSoc/main_test.py

@@ -0,0 +1,128 @@
+import datetime
+import pandas as pd
+import time
+from apscheduler.schedulers.blocking import BlockingScheduler
+from USER.alibaba.Common import DBDownload
+from USER.alibaba.Common import log
+import CBMSBatSoc
+
+#...................................SOC计算函数......................................................................................................................
+def soc_cal():
+    global start_time
+    global df_ram
+    global df_bms
+    SNnums= ['HUATING01']
+
+    start=time.time()
+    # end_time=datetime.datetime.now()-datetime.timedelta(seconds=10)
+    # start_time=end_time-datetime.timedelta(seconds=11)
+    
+    start_time=start_time+datetime.timedelta(seconds=10)
+    end_time=start_time+datetime.timedelta(seconds=11)
+    # start_time=start_time.strftime('%Y-%m-%d %H:%M:%S')
+    # end_time=end_time.strftime('%Y-%m-%d %H:%M:%S')
+
+    for sn in SNnums:
+        # if 'PK500' in sn:
+        #     celltype=1 #6040三元电芯
+        # elif 'PK502' in sn:
+        #     celltype=2 #4840三元电芯
+        # elif 'K504B' in sn:
+        #     celltype=99    #60ah林磷酸铁锂电芯
+        # elif 'MGMLXN750' in sn:
+        #     celltype=3 #力信50ah三元电芯
+        # elif 'MGMCLN750' or 'UD' in sn: 
+        #     celltype=4 #CATL 50ah三元电芯
+        # else:
+        #     print('SN：{}，未找到对应电池类型！！！'.format(sn))
+        #     continue
+            # sys.exit()
+
+        celltype=2
+        # start_time='2021-05-02 09:12:26'
+        # end_time='2021-06-03 19:12:26'
+        # # df_bms= pd.read_csv(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\98Download\\'+'BMS_'+sn+'.csv',encoding='GB18030')
+
+        #读取原始数据库数据........................................................................................................................................................
+        # df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-SOH原始数据.xlsx')
+        # df_bms.to_csv(r'D:\Platform\platform_python\data_analyze_platform\USER\spf\01qixiang\99Result\\''BMS_'+sn+'.csv',encoding='GB18030')
+        # df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+        df_bms1=df_bms[(df_bms['time'] > start_time) & (df_bms['time'] < end_time)]
+        df_bms1.reset_index(inplace=True,drop=True)
+        # print(df_bms1)
+
+        #读取结果数据库数据........................................................................................................................................................
+        # host='rm-bp10j10qy42bzy0q77o.mysql.rds.aliyuncs.com'
+        # port=3306
+        # db='qx_cas'
+        # user='qx_read'
+        # password='Qx@123456'
+        # mode=1
+        # tablename='cellstateestimation_soh'
+        # DBRead=DBDownload.DBDownload(host, port, db, user, password, mode)
+        # with DBRead as DBRead:
+        #     df_soh=DBRead.getdata(param='time_st,sn,soh', tablename=tablename, sn=sn, timename='time_sp', st=start_time, sp=end_time)
+        df_soh=pd.DataFrame({'time_st':[1],'sn':[sn],'soh':[100]})
+
+        if celltype>50:
+            # tablename='cellstateestimation_uniform_socvoltdiff'
+            # with DBRead as DBRead:
+            #     df_socdiff=DBRead.getdata(param='time,sn,cellsoc_diff', tablename=tablename, sn=sn, timename='time', st=start_time, sp=end_time)
+            df_socdiff=pd.DataFrame({'time':[1],'sn':[sn],'cellsoc_diff':[1.2]})
+        else:
+            df_socdiff=pd.DataFrame()
+
+        # print(df_bms)
+        # print(df_soh)
+        
+        #socram存储...............................................................................................................................................................
+        if not df_bms1.empty:
+            df_ram_sn=df_ram[df_ram['sn']==sn]
+
+            if df_ram_sn.empty:
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\04BatSoc\\'+'soc_'+sn+'.txt','a') as file:
+                    file.write(str(tuple(df_ram_sn.columns))+'\n')
+            
+            BatSoc=CBMSBatSoc.BatSoc(sn,celltype,df_bms1,df_soh,df_ram_sn,df_socdiff)
+            df_res, df_ram_sn=BatSoc.batsoc()
+            if not df_ram_sn.empty:
+                sn_index=df_ram.loc[df_ram['sn']==sn].index
+                df_ram=df_ram.drop(index=sn_index)
+                df_ram=df_ram.append(df_ram_sn)
+                df_ram.reset_index(inplace=True,drop=True)     #重置索引
+            
+            # print(df_res)
+            # print(df_ram)
+
+            if not df_ram_sn.empty:
+                with open(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\04BatSoc\\'+'soc_'+sn+'.txt','a') as file:
+                    file.write(str(tuple(df_ram_sn.iloc[-1]))+'\n')
+                    
+        
+    end=time.time()
+    print(end-start)
+        
+#...............................................主函数.......................................................................................................................
+if __name__ == "__main__":
+
+    #log信息配置
+    mylog=log.Mylog('log_soc.txt','error')
+    mylog.logcfg()
+
+    #参数初始化.........................................................................................................................................................................
+    column_name=['time', 'sn', 'bms_soc', 'soc','cellsoc','standingtime','rampackcrnt','ramcellvolt','kocellvoltmin','kocellvoltmax','ocvweight','as_accum','socstep']
+    df_ram=pd.DataFrame(columns=column_name)
+    start_time=datetime.datetime.strptime('2021-10-22 14:59:00','%Y-%m-%d %H:%M:%S')
+    df_bms= pd.read_excel(r'D:\00WorkSpace\01Python\data_analyze_platform\USER\alibaba\98Download\18650-SOC原始数据.xlsx')
+    df_bms['time']=pd.to_datetime(df_bms['time'], format='%Y-%m-%d %H:%M:%S')
+
+    scheduler = BlockingScheduler()
+    scheduler.add_job(soc_cal, 'interval', seconds=0.05, id='soc_job')
+    
+    try:
+        scheduler.start()
+
+    except Exception as e:
+        scheduler.shutdown()
+        print(repr(e))
+        mylog.logopt(e)