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+# 获取数据
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+from LIB.BACKEND import DBManager
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+
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+import os
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+import pandas as pd
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+import numpy as np
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+import bisect
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+import datetime
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+# import matplotlib.pyplot as plt
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+
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+#参数输入
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+Capacity = 53.6
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+PackFullChrgVolt=69.99
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+CellFullChrgVolt=3.37
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+CellVoltNums=20
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+CellTempNums=4
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+FullChrgSoc=98
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+CellVoltPort=[3.357,3.358,3.359,3.36,3.361]
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+PeakSoc=57
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+# #40Ah-OCV
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+# LookTab_SOC = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100]
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+# LookTab_OCV = [3.3159, 3.4502, 3.4904, 3.5277, 3.5590, 3.5888, 3.6146, 3.6312, 3.6467, 3.6642, 3.6865, 3.7171, 3.7617,
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+# 3.8031, 3.8440, 3.8888, 3.9376, 3.9891, 4.0451, 4.1068, 4.1830]
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+#55Ah-OCV
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+LookTab_SOC = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
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+LookTab_OCV = [3.1820, 3.2250, 3.2730, 3.2840, 3.2860, 3.2920, 3.3210, 3.3260, 3.3270, 3.3270, 3.3640]
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+
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+# 获取数据时间段
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+def cal_LFPLeakCurrent(sn, end_time, start_time):
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+ end_time = end_time
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+ strat_time = start_time
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+
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+ sn = sn
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+ st = strat_time
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+ et = end_time
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+
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+ dbManager = DBManager.DBManager()
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+ df_data = dbManager.get_data(sn=sn, start_time=st, end_time=et, data_groups=['bms'])
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+ df_bms = df_data['bms']
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+
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+ #寻找电压最大值
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+ packcrnt=df_bms['总电流[A]']
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+ SOC=df_bms['SOC[%]']
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+ bmsstat=df_bms['充电状态']
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+ time= pd.to_datetime(df_bms['时间戳'], format='%Y-%m-%d %H:%M:%S')
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+
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+ #第一步：筛选充电数据
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+ ChgStart=[]
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+ ChgEnd=[]
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+ for i in range(3, len(time) - 3):
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+ if i==3 and bmsstat[i]==2 and bmsstat[i+1]==2 and bmsstat[i+2]==2:
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+ ChgStart.append(i)
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+ elif bmsstat[i-2]!=2 and bmsstat[i-1]!=2 and bmsstat[i]==2:
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+ ChgStart.append(i)
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+ elif bmsstat[i-1]==2 and bmsstat[i]!=2 and bmsstat[i+1]!=2:
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+ ChgEnd.append(i)
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+ elif i == (len(time) - 4) and bmsstat[len(bmsstat)-1] == 2 and bmsstat[len(bmsstat)-2] == 2:
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+ ChgEnd.append(len(time)-1)
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+
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+ #第二步：筛选充电起始Soc<45%，且单体最小电压>3.37V的数据
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+ ChgStartValid=[]
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+ ChgEndValid=[]
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+ if ChgStart:
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+ for i in range(len(ChgEnd)):
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+ #寻找最小电压值
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+ cellvolt = []
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+ for j in range(1, CellVoltNums+1):
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+ s = str(j)
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+ volt = df_bms['单体电压' + s]/1000
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+ cellvolt.append(max(volt[ChgStart[i]:ChgEnd[i]]))
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+ if min(cellvolt)>CellFullChrgVolt and SOC[ChgStart[i]]<40 and (ChgEnd[i]-ChgStart[i])>10:
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+ if ((time[ChgEnd[i]]-time[ChgStart[i]]).total_seconds())/(ChgEnd[i]-ChgStart[i])<30:
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+ ChgStartValid.append(ChgStart[i])
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+ ChgEndValid.append(ChgEnd[i])
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+
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+ #第三步：计算充电每个单体到达3.368V的Ah差
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+ #定义寻找电压3.368V的数据点
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+ def data_search(data1,data2,data3,data4):
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+ Soc=0
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+ for m in range(1,len(data1)):
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+ t=(data2[m]-data2[m-1]).total_seconds()
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+ Soc=Soc-data3[m]*t/(3600*Capacity)
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+ if data1[m]>data4:
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+ DetaT=(data2[m]-data2[0]).total_seconds()
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+ return Soc,m
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+ break
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+
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+ if ChgStartValid:
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+ for i in range(len(ChgStartValid)):
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+ DetaSoc1=[]
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+ DetaSoc2 = []
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+ DetaSoc=[]
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+ a=list(range(5))
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+ b=list(range(5))
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+ #计算1-10号电芯到达特定电压值得时间和SOC
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+ for j in range(1, CellVoltNums-9):
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+ s = str(j)
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+ cellvolt = df_bms['单体电压' + s]/1000
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+ cellvolt=list(cellvolt[ChgStartValid[i]:ChgEndValid[i]])
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+ Time=list(time[ChgStartValid[i]:ChgEndValid[i]])
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+ Packcrnt=list(packcrnt[ChgStartValid[i]:ChgEndValid[i]])
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+ for k in range(len(CellVoltPort)):
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+ a[k],b[k]=data_search(cellvolt,Time,Packcrnt,CellVoltPort[k])
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+ DetaSoc1.append(np.mean(a)) #计算到达3.368V的时长
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+ # DetaT.append((Time[b]-Time[0]).total_seconds())
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+
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+ #计算1-10号电芯到达特定电压值的平均Soc
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+ Socmean1=(sum(DetaSoc1)-max(DetaSoc1)-min(DetaSoc1))/(len(DetaSoc1)-2)
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+ # Tmean=np.mean(DetaT)
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+
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+ ##计算11-20号电芯到达特定电压值得时间和SOC
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+ for j in range(11, CellVoltNums+1):
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+ s = str(j)
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+ cellvolt = df_bms['单体电压' + s]/1000
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+ cellvolt=list(cellvolt[ChgStartValid[i]:ChgEndValid[i]])
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+ Time=list(time[ChgStartValid[i]:ChgEndValid[i]])
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+ Packcrnt=list(packcrnt[ChgStartValid[i]:ChgEndValid[i]])
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+ for k in range(len(CellVoltPort)):
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+ a[k],b[k]=data_search(cellvolt,Time,Packcrnt,CellVoltPort[k])
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+ DetaSoc2.append(np.mean(a)) #计算到达3.368V的时长
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+
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+ #计算11-20号电芯到达特定电压值的平均Soc
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+ Socmean2=(sum(DetaSoc2)-max(DetaSoc2)-min(DetaSoc2))/(len(DetaSoc2)-2)
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+
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+ #计算每个电芯的Soc差
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+ df_DetaTime=pd.DataFrame()
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+ df_detatime=pd.DataFrame()
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+ DetaSoc3=DetaSoc1+DetaSoc2
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+ for j in range(len(DetaSoc3)):
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+ if j<10:
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+ Socmean=Socmean1
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+ else:
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+ Socmean=Socmean2
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+ DetaSoc.append(DetaSoc3[j]-Socmean)
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+ # DetaSoc.append((DetaT[j]-Tmean)*9.5/(Capacity*3600))
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+ df_DetaTime[time[ChgStartValid[i]]]=DetaSoc
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+
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+ #漏电流计算
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+ column=[]
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+ time1=[]
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+ sn1=[]
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+
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+ for index, row in df_DetaTime.iteritems():
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+ column.append(index) #提取列名称
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+
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+ for i in range(1,len(column)):#计算漏电流值
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+ df_DetaTime[column[i]] = df_DetaTime.apply(lambda x: (x[column[i-1]] - x[column[i]])*1000*Capacity*3600/((column[i]-column[i-1]).total_seconds()), axis=1)
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+ time1.append(column[i])
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+ sn1.append(sn)
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+ df_detatime['time']=time1
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+ df_detatime['sn']=sn1
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+
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+ for i in range(len(CellVoltNums)):
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+ cell=[]
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+ for j in range(1,len(column)):
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+ cell.append(df_DetaTime[1,column[j]])
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+ df_detatime['cell'+str(i+1)]=cell
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+ return df_detatime
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+ return pd.DataFrame()
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