更新SOH计算程序

LIB/MIDDLE/CellStateEstimation/SOH/V1_0_0/CloudBMSSoh.py → LIB/MIDDLE/CellStateEstimation/SOH/V1_0_0/CBMSBatSoh.py

@@ -6,7 +6,7 @@ import matplotlib.pyplot as plt
 import BatParam
 import DBDownload
 
-class Soh:
+class BatSoh:
     def __init__(self,sn,df_bms,df_accum):  #参数初始化
 
         self.sn=sn
@@ -22,7 +22,7 @@ class Soh:
         self.df_accum=df_accum
         self.accumtime=pd.to_datetime(df_accum['时间戳'], format='%Y-%m-%d %H:%M:%S')
 
-    def soh(self):
+    def batsoh(self):
         if self.param.celltype==1 or self.param.celltype==2:
             df_res=self._ncmsoh_twopoint()
             return df_res
@@ -46,10 +46,10 @@ class Soh:
 
         DBManager=DBDownload.DBDownload(host, port, db, user, password)
         with DBManager as DBManager:
-            df_soh=DBManager.getdata('sn', 'time', 'bms_soh', 'soh', 'soh_err', tablename='soh_result',st=start_time,sp=end_time)
+            df_soh=DBManager.getdata('sn', 'time', 'bms_soh', 'soh', 'soh_err', tablename='soh_result_old',st=start_time,sp=end_time)
             self.df_soh=df_soh[df_soh['sn']==self.sn]
     
-    def np_move_avg(self,a, n, mode="same"): #定义滑动滤波函数
+    def _np_move_avg(self,a, n, mode="same"): #定义滑动滤波函数
         return (np.convolve(a, np.ones((n,)) / n, mode=mode))
 
     def _chrgdata(self):    #筛选充电数据
@@ -345,11 +345,7 @@ class Soh:
                     soh_value=cellsoh[4:4+self.param.CellVoltNums]
                     soh=min(soh_value)
                     cellsoh.insert(4,soh)
-                    if len(df_res)<1:
-                        df_res.loc[0]=cellsoh
-                    else:
-                        df_res.loc[df_res.index.values[-1]+1]=cellsoh
-                        print(df_res)
+                    df_res.loc[len(df_res)]=cellsoh
                 else:
                     continue
             if df_res.empty:
@@ -473,7 +469,7 @@ class Soh:
                         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)-4 and self.packcrnt[i+1]<-1 and self.packcrnt[i+2]<-1:
                     charging=0
                     if len(chrg_start)>len(chrg_end):
                         cellvolt_now=self._cellvolt_get(i)
@@ -583,10 +579,7 @@ class Soh:
                         cellsoh.append(cellsoh_cal)
                         for j in range(20):    #计算每个电芯的SOH值
                             cellsoh.append(cellsoh_cal)
-                        if len(df_res)<1:
-                            df_res.loc[0]=cellsoh
-                        else:
-                            df_res.loc[df_res.index.values[-1]+1]=cellsoh
+                        df_res.loc[len(df_res)]=cellsoh
                     else:
                         continue
             else:
@@ -595,7 +588,7 @@ class Soh:
             if chrg_end:
                 for i in range(len(chrg_end)):
                     cellvolt_max = self.df_bms['单体电压' + str(cellmaxvolt_number2[i]+1)] / 1000     #获取最大电压
-                    cellvolt=self.np_move_avg(cellvolt_max, 3, mode="same")     #对电压进行滑动平均滤波
+                    cellvolt=self._np_move_avg(cellvolt_max, 3, mode="same")     #对电压进行滑动平均滤波
                     
                     Ah = 0  #参数赋初始值
                     Volt = cellvolt[chrg_start[i]]
@@ -622,40 +615,40 @@ class Soh:
                             soc2.append(self.bms_soc[j])
 
                     #切片，去除前后10min的数据
-                    Data1 = pd.DataFrame({'time': time2,
+                    df_Data1 = pd.DataFrame({'time': time2,
                                             'SOC': soc2,
                                             'DVDQ': DVDQ,
                                             'Ah_tatal': Ah_tatal[:-1],
                                             'DQ_Ah':DQ_Ah,
                                             'DV_Volt':DV_Volt,
                                             'XVOLT':xvolt})
-                    start_time=Data1.loc[0,'time']
+                    start_time=df_Data1.loc[0,'time']
                     start_time=start_time+datetime.timedelta(seconds=600)
-                    end_time=Data1.loc[len(time2)-1,'time']
+                    end_time=df_Data1.loc[len(time2)-1,'time']
                     end_time=end_time-datetime.timedelta(seconds=1200)
                     if soc2[0]<37:
-                        Data1=Data1[(Data1['SOC']>39) & (Data1['time']<end_time)]
+                        df_Data1=df_Data1[(df_Data1['SOC']>39) & (df_Data1['time']<end_time)]
                     else:
-                        Data1=Data1[(Data1['time']>start_time) & (Data1['time']<end_time)]
+                        df_Data1=df_Data1[(df_Data1['time']>start_time) & (df_Data1['time']<end_time)]
             
                     # ax1 = plt.subplot(3, 1, 1)
-                    # plt.plot(Data1['XVOLT'],Data1['DVDQ'],'r*-')
+                    # plt.plot(df_Data1['XVOLT'],df_Data1['DVDQ'],'r*-')
                     # ax1 = plt.subplot(3, 1, 2)
-                    # plt.plot(Data1['SOC'],Data1['XVOLT'],'y*-')
+                    # plt.plot(df_Data1['SOC'],df_Data1['XVOLT'],'y*-')
                     # ax1 = plt.subplot(3, 1, 3)
-                    # plt.plot(Data1['SOC'], Data1['DVDQ'], 'r*-')
+                    # plt.plot(df_Data1['SOC'], df_Data1['DVDQ'], 'r*-')
                     # plt.show()
 
                     #寻找峰值并计算Soh和置信度
-                    if len(Data1['DVDQ'])>1:
-                        PeakIndex=Data1['DVDQ'].idxmax()
+                    if len(df_Data1)>1:
+                        PeakIndex=df_Data1['DVDQ'].idxmax()
                         #筛选峰值点附近±0.5%SOC内的数据
-                        Data2=Data1[(Data1['SOC']>(Data1['SOC'][PeakIndex]-0.5)) & (Data1['SOC']<(Data1['SOC'][PeakIndex]+0.5))]
+                        Data2=df_Data1[(df_Data1['SOC']>(df_Data1['SOC'][PeakIndex]-0.5)) & (df_Data1['SOC']<(df_Data1['SOC'][PeakIndex]+0.5))]
                         if len(Data2)>2:
-                            Ah_tatal1 = Data1['Ah_tatal']
-                            DVDQ = Data1['DVDQ']
-                            soc2 = Data1['SOC']
-                            xvolt = Data1['XVOLT']
+                            Ah_tatal1 = df_Data1['Ah_tatal']
+                            DVDQ = df_Data1['DVDQ']
+                            soc2 = df_Data1['SOC']
+                            xvolt = df_Data1['XVOLT']
                             if soc2[PeakIndex]>40 and soc2[PeakIndex]<90:
                                 cellsoh_init=(Ah_tatal[-1]-Ah_tatal1[PeakIndex]) * 100 / ((self.param.FullChrgSoc - self.param.PeakSoc) * 0.01 * self.param.Capacity)
                                 if cellsoh_init<95:
@@ -665,14 +658,14 @@ class Soh:
                             else:
                                 continue
                         else:
-                            Data1=Data1.drop([PeakIndex])
-                            PeakIndex = Data1['DVDQ'].idxmax()
-                            Data2 = Data1[(Data1['SOC'] > (Data1['SOC'][PeakIndex] - 0.5)) & (Data1['SOC'] < (Data1['SOC'][PeakIndex] + 0.5))]
+                            df_Data1=df_Data1.drop([PeakIndex])
+                            PeakIndex = df_Data1['DVDQ'].idxmax()
+                            Data2 = df_Data1[(df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
                             if len(Data2) > 2:
-                                Ah_tatal1 = Data1['Ah_tatal']
-                                DVDQ = Data1['DVDQ']
-                                soc2 = Data1['SOC']
-                                xvolt = Data1['XVOLT']
+                                Ah_tatal1 = df_Data1['Ah_tatal']
+                                DVDQ = df_Data1['DVDQ']
+                                soc2 = df_Data1['SOC']
+                                xvolt = df_Data1['XVOLT']
                                 if soc2[PeakIndex]>40 and soc2[PeakIndex]<90:
                                     cellsoh_init=(Ah_tatal[-1]-Ah_tatal1[PeakIndex]) * 100 / ((self.param.FullChrgSoc - self.param.PeakSoc) * 0.01 * self.param.Capacity)
                                     if cellsoh_init<95:
@@ -703,10 +696,7 @@ class Soh:
                             cellsoh.append(cellsoh_cal)
                             for j in range(20):    #计算每个电芯的SOH值
                                 cellsoh.append(cellsoh_cal)
-                            if len(df_res)<1:
-                                df_res.loc[0]=cellsoh
-                            else:
-                                df_res.loc[df_res.index.values[-1]+1]=cellsoh
+                            df_res.loc[len(df_res)]=cellsoh
                         else:
                             continue
                     else:

LIB/MIDDLE/CellStateEstimation/SOH/main.py

@@ -1,4 +1,4 @@
-import CloudBMSSoh
+import CBMSBatSoh
 import log
 
 #coding=utf-8
@@ -29,7 +29,7 @@ if __name__ == "__main__":
     SNnums=SNnums_6060.tolist()+SNnums_6040.tolist()+SNnums_4840.tolist()
     now_time=datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
     now_time=datetime.datetime.strptime(now_time,'%Y-%m-%d %H:%M:%S')
-    start_time=now_time-datetime.timedelta(days=30*36)
+    start_time=now_time-datetime.timedelta(days=30)
     end_time=str(now_time)
     start_time=str(start_time)
 
@@ -46,8 +46,8 @@ if __name__ == "__main__":
             # df_bms.to_csv('BMS_'+sn+'.csv')
             # df_accum.to_csv('BMS_accum_'+sn+'.csv')
 
-            Soh_cal=CloudBMSSoh.Soh(sn,df_bms,df_accum)
-            df_res=Soh_cal.soh()
+            BatSoh=CBMSBatSoh.BatSoh(sn,df_bms,df_accum)
+            df_res=BatSoh.batsoh()
             df_res.to_csv('BMS_SOH_'+sn+'.csv',encoding='GB18030')