4842 SOH

LIB/MIDDLE/soh/NCMSoh_4842.py

@@ -0,0 +1,169 @@
+# 获取数据
+from LIB.BACKEND import DBManager
+
+import os
+import pandas as pd
+import numpy as np
+import datetime
+# import matplotlib.pyplot as plt
+
+#参数输入
+Capacity = 41
+PackFullChrgVolt=69.99
+CellFullChrgVolt=3.5
+CellVoltNums=14
+CellTempNums=4
+FullChrgSoc=98
+PeakSoc=57
+# #40Ah-OCV
+LookTab_SOC = [0,	3.534883489,	8.358178409,	13.18141871,	18.00471528,	22.82796155,	27.65123833,	32.47444668,	37.29772717,	42.12099502,	46.94423182,	51.76744813,	56.59070685,	61.4139927,	66.23719857,	71.0604667,	75.88373853,	80.70702266,	85.5302705,	90.35352009,	95.17676458,	100]
+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]
+# #55Ah-OCV
+# 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]
+# 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]
+
+#参数初始化
+Soh3=[]
+Time3=[]
+Bms_Soh3=[]
+Soh_Err3=[]
+sn_list=[]
+
+#获取数据时间段
+
+def cal_soh(sn, end_time, start_time):
+    end_time = end_time
+    strat_time = start_time
+    SNnum=str(sn)
+
+    sn = sn
+    st = strat_time
+    et = end_time
+
+    dbManager = DBManager.DBManager()
+    df_data = dbManager.get_data(sn=sn, start_time=st, end_time=et, data_groups=['bms'])
+    data = df_data['bms']
+    # print(data)
+
+    packcrnt=data['总电流[A]']
+    packvolt=data['总电压[V]']
+    SOC=data['SOC[%]']
+    SOH=data['SOH[%]']
+    bmsstat=data['充电状态']
+    time= pd.to_datetime(data['时间戳'], format='%Y-%m-%d %H:%M:%S')
+
+    #第一步：筛选充电数据
+    if len(packcrnt)>100:
+        ChgStart=[]
+        ChgEnd=[]
+        for i in range(3, len(time) - 3):
+            if i==3 and bmsstat[i]==2 and bmsstat[i+1]==2 and bmsstat[i+2]==2:
+                ChgStart.append(i)
+            elif bmsstat[i-2]!=2 and bmsstat[i-1]!=2 and bmsstat[i]==2:
+                ChgStart.append(i)
+            elif bmsstat[i-1]==2 and bmsstat[i]!=2 and bmsstat[i+1]!=2:
+                ChgEnd.append(i-1)
+            elif i == (len(time) - 4) and bmsstat[len(bmsstat)-1] == 2 and bmsstat[len(bmsstat)-2] == 2:
+                ChgEnd.append(len(time)-2)
+
+        #第二步：筛选充电起始Soc<45% & SOC>85%，电芯温度>5℃
+        ChgStartValid1=[]
+        ChgEndValid1=[]
+        ChgStartValid2=[]
+        ChgEndValid2=[]
+        StandingNum=[]
+
+        for i in range(min(len(ChgStart),len(ChgEnd))):
+
+            #获取最小温度值
+            celltemp = []
+            for j in range(1, CellTempNums+1):
+                s = str(j)
+                temp = data['单体温度' + s]
+                celltemp.append(temp[ChgEnd[i]])
+            
+            #去除电流0点   
+            for k in range(ChgStart[i],ChgEnd[i]):
+                if packcrnt[k]<-0.5 and packcrnt[k+1]>-0.5 and packcrnt[k+2]>-0.5 and packcrnt[k+3]>-0.5:
+                    ChgEnd[i]=k
+            
+            #计算最大packvolt
+            if len(packvolt[ChgStart[i]:ChgEnd[i]])>0:
+                packvoltMAX=max(packvolt[ChgStart[i]:ChgEnd[i]])
+
+                #筛选满足2点法计算的数据
+                StandingTime=0
+                StandingTime1=0
+                StandingTime2=0
+                if SOC[ChgEnd[i]]>85 and SOC[ChgStart[i]]<45 and min(celltemp)>5:
+                    for m in range(min(len(packcrnt)-ChgEnd[i]-2,ChgStart[i]-2)):
+                        if abs(packcrnt[ChgStart[i] - m - 1]) < 0.1:
+                            StandingTime = StandingTime + (time[ChgStart[i] - m] - time[ChgStart[i] - m - 1]).total_seconds()
+                        if abs(packcrnt[ChgEnd[i] + m + 1]) < 0.1:
+                            StandingTime1 = StandingTime1 + (time[ChgEnd[i] + m + 1] - time[ChgEnd[i] + m]).total_seconds()
+                        if StandingTime > 900 and StandingTime1>900 and ((time[ChgEnd[i]]-time[ChgStart[i]]).total_seconds())/(ChgEnd[i]-ChgStart[i])<60:  #筛选静置时间>15min且慢充过程丢失数据少
+                            ChgStartValid1.append(ChgStart[i])
+                            ChgEndValid1.append(ChgEnd[i])
+                            StandingNum.append(m)
+                            break
+                        if abs(packcrnt[ChgStart[i] - m - 2])>0.5 and abs(packcrnt[ChgEnd[i] + m + 2])>0.5:
+                            break
+
+        # 计算soh
+        Soh1=[]
+        Soh2=[]
+        Time1=[]
+        Bms_Soh1=[]
+        Soh_Err1=[]
+        sn_list1=[]
+        #两点法计算Soh
+        if len(ChgStartValid1)>0:
+            for i in range(len(ChgStartValid1)):
+                #计算Ah
+                Ah=0
+                for j in range(ChgStartValid1[i],ChgEndValid1[i]):
+                    Step=(time[j+1]-time[j]).total_seconds()
+                    Ah=Ah-packcrnt[j+1]*Step/3600
+                #计算每个电芯的Soh
+                for j in range(1, CellVoltNums+1):
+                    s = str(j)
+                    cellvolt = data['单体电压' + s]/1000
+                    OCVStart=cellvolt[ChgStartValid1[i]-2]
+                    OCVEnd=cellvolt[ChgEndValid1[i]+StandingNum[i]]
+                    #soh
+                    Ocv_Soc1=np.interp(OCVStart,LookTab_OCV,LookTab_SOC)
+                    Ocv_Soc2=np.interp(OCVEnd,LookTab_OCV,LookTab_SOC)
+                    Soh2.append(Ah*100/((Ocv_Soc2-Ocv_Soc1)*0.01*Capacity))
+                Soh1.append(np.mean(Soh2))
+                Bms_Soh1.append(SOH[ChgStartValid1[i]])
+                Soh_Err1.append(Bms_Soh1[-1]-Soh1[-1])
+                Time1.append(time[ChgStartValid1[i]])
+                sn_list1.append(SNnum)
+       
+            # Soh3.append(np.mean(Soh1))
+            # Bms_Soh3.append(np.mean(Bms_Soh1))
+            # Soh_Err3.append(np.mean(Soh_Err1))
+            # Time3.append(time[ChgStartValid1[-1]])
+            # sn_list.append(SNnum)
+
+        #第四步：将数据存入Excel
+            result_soh2={'时间': Time1,
+                'SN号': sn_list1,
+                'BMS_SOH': Bms_Soh1,
+                'SOH': Soh1,
+                'SOH误差': Soh_Err1}
+
+            Result_Soh2=pd.DataFrame(result_soh2)
+            # Result_Soh2.to_csv('BMS_SOH_'+SNnum+'.csv',encoding='GB18030')
+            return Result_Soh2
+    return pd.DataFrame()
+
+#     result_soh1={'时间': Time3,
+#         'SN号':sn_list,
+#         'BMS_SOH': Bms_Soh3,
+#         'SOH': Soh3,
+#         'SOH误差': Soh_Err3}
+
+# Result_Soh1=pd.DataFrame(result_soh1)
+# print(Result_Soh1)
+# Result_Soh1.to_csv('BMS_SOH_'+'6040'+'.csv',encoding='GB18030')