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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 datetime
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+# import matplotlib.pyplot as plt
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+#参数初始化
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+Capacity = 53.6
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+PackFullChrgVolt=69.99
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+CellFullChrgVolt=3.6
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+CellVoltNums=20
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+CellTempNums=4
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+FullChrgSoc=98
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+PeakSoc=57
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+
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+#获取数据时间段
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+def cal_deltsoc(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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+ SNnum=str(sn)
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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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+
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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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+ # 计算电芯Soc差
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+ packcrnt = df_bms['总电流[A]']
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+ packvolt = df_bms['总电压[V]']
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+ SOC = df_bms['SOC[%]']
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+ SOH = df_bms['SOH[%]']
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+ bmsstat = (df_bms['充电状态']).astype(int)
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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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+ # 筛选充电数据
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+ ChgStart = []
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+ ChgEnd = []
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+
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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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+ # 筛选充电起始Soc<46%，电芯温度>15℃的数据
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+ ChgStartValid = []
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+ ChgEndValid = []
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+
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+ for i in range(min(len(ChgStart),len(ChgEnd))):
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+ # 获取最小温度值
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+ celltemp = []
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+ for j in range(1, CellTempNums + 1):
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+ s = str(j)
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+ temp = df_bms['单体温度' + s]
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+ celltemp.append(temp[ChgEnd[i]])
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+
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+ if SOC[ChgStart[i]] < 46 and SOC[ChgEnd[i]]>80 and min(celltemp) > 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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+ # 计算充电每个单体到达DVDQ峰值的Ah差
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+ # 定义滑动平均滤波函数
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+ def np_move_avg(a, n, mode="same"):
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+ return (np.convolve(a, np.ones((n,)) / n, mode=mode))
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+
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+
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+ # 定义函数：切片Soc>50且Soc<80，并寻找峰值返回峰值点的时间
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+ def data_search(t, soc, cellvolt1, packcrnt1):
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+ cellvolt2 = np_move_avg(cellvolt1, 5, mode="same")
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+ Soc = 0
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+ Ah = 0
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+ Volt = [cellvolt2[0]]
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+ DV_Volt = []
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+ DQ_Ah = []
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+ DVDQ = []
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+ time1 = []
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+ soc1 = []
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+ soc2 = []
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+
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+ for m in range(1, len(t)):
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+ Step = (t[m] - t[m - 1]).total_seconds()
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+ Soc = Soc - packcrnt1[m] * Step * 100 / (3600 * Capacity)
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+ Ah = Ah - packcrnt1[m] * Step / 3600
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+ if (cellvolt2[m] - Volt[-1]) > 0.9 and Ah>0:
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+ DQ_Ah.append(Ah)
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+ Volt.append(cellvolt2[m])
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+ DV_Volt.append(Volt[-1] - Volt[-2])
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+ DVDQ.append((DV_Volt[-1]) / DQ_Ah[-1])
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+ Ah = 0
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+ time1.append(t[m])
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+ soc1.append(Soc)
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+ soc2.append(soc[m])
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+ df_Data1 = pd.DataFrame({'Time': time1,
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+ 'SOC': soc2,
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+ 'DVDQ': DVDQ,
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+ 'AhSOC': soc1})
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+
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+ df_Data1 = df_Data1[(df_Data1['SOC'] > 50) & (df_Data1['SOC'] < 75)]
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+ # 寻找峰值点，且峰值点个数>3
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+ if len(df_Data1['DVDQ'])>2:
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+ PeakIndex = df_Data1['DVDQ'].idxmax()
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+ df_Data2 = df_Data1[
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+ (df_Data1['SOC'] > (df_Data1['SOC'][PeakIndex] - 0.5)) & (df_Data1['SOC'] < (df_Data1['SOC'][PeakIndex] + 0.5))]
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+ if len(df_Data2) > 3:
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+ return df_Data1['AhSOC'][PeakIndex]
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+ else:
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+ df_Data1 = df_Data1.drop([PeakIndex])
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+ PeakIndex = df_Data1['DVDQ'].idxmax()
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+ return df_Data1['AhSOC'][PeakIndex]
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+
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+
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+ # 计算最大最小Soc差
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+ if ChgStartValid:
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+ DetaSoc2 = []
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+ DetaSoc=[]
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+ DetaSoc_SN=[]
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+ DetaSoc_time=[]
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+ for i in range(len(ChgStartValid)):
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+ DetaSoc1 = []
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+ for j in range(1, CellVoltNums + 1):
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+ s = str(j)
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+ cellvolt = df_bms['单体电压' + s]
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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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+ SOC1 = list(SOC[ChgStartValid[i]:ChgEndValid[i]])
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+ a = data_search(Time, SOC1, cellvolt, Packcrnt)
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+ if a:
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+ DetaSoc1.append(a) # 计算到达峰值点的累计Soc
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+ if DetaSoc1:
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+ DetaSoc2.append(max(DetaSoc1) - min(DetaSoc1))
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+
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+ DetaSocMean = np.mean(DetaSoc2)
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+ DetaSoc.append(DetaSocMean)
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+ DetaSoc_SN.append(SNnum)
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+ DetaSoc_time.append(time[ChgStartValid[-1]])
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+
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+
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+ result_DetaSoc={'time':DetaSoc_time,
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+ 'SN号':DetaSoc_SN,
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+ 'Soc差':DetaSoc}
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+ Result_DetaSoc=pd.DataFrame(result_DetaSoc)
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+ return Result_DetaSoc
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+ return pd.DataFrame()
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+
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+
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