data_analyze_platform/LIB/MIDDLE/DeltSoc/LFPDeltSoc20210804.py

# 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
#参数初始化
Capacity = 53.6
PackFullChrgVolt=69.99
CellFullChrgVolt=3.6
CellVoltNums=20
CellTempNums=4
FullChrgSoc=98
PeakSoc=57

#获取数据时间段
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=2)
end_time=str(now_time)
strat_time=str(start_time)

#输入一个含有‘SN号’的xlsx
SNdata = pd.read_excel('骑享资产梳理-20210621.xlsx', sheet_name='6060')
SNnums=SNdata['SN号']
for k in range(len(SNnums)):
    SNnum=str(SNnums[k])

    sn = SNnum
    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'])
    df_bms = df_data['bms']
    # 计算电芯Soc差
    packcrnt = df_bms['总电流[A]']
    packvolt = df_bms['总电压[V]']
    SOC = df_bms['SOC[%]']
    SOH = df_bms['SOH[%]']
    bmsstat = (df_bms['充电状态']).astype(int)
    time = pd.to_datetime(df_bms['时间戳'], format='%Y-%m-%d %H:%M:%S')

    # 筛选充电数据
    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)
        elif i == (len(time) - 4) and bmsstat[len(bmsstat)-1] == 2 and bmsstat[len(bmsstat)-2] == 2:
            ChgEnd.append(len(time)-1)

    # 筛选充电起始Soc<46%，电芯温度>15℃的数据
    ChgStartValid = []
    ChgEndValid = []

    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 = df_bms['单体温度' + s]
            celltemp.append(temp[ChgEnd[i]])

        if SOC[ChgStart[i]] < 46 and SOC[ChgEnd[i]]>80 and min(celltemp) > 10:
            if ((time[ChgEnd[i]]-time[ChgStart[i]]).total_seconds())/(ChgEnd[i]-ChgStart[i])<30:
                ChgStartValid.append(ChgStart[i])
                ChgEndValid.append(ChgEnd[i])

    # 计算充电每个单体到达DVDQ峰值的Ah差
    # 定义滑动平均滤波函数
    def np_move_avg(a, n, mode="same"):
        return (np.convolve(a, np.ones((n,)) / n, mode=mode))


    # 定义函数：切片Soc>50且Soc<80，并寻找峰值返回峰值点的时间
    def data_search(t, soc, cellvolt1, packcrnt1):
        cellvolt2 = np_move_avg(cellvolt1, 5, mode="same")
        Soc = 0
        Ah = 0
        Volt = [cellvolt2[0]]
        DV_Volt = []
        DQ_Ah = []
        DVDQ = []
        time1 = []
        soc1 = []
        soc2 = []

        for m in range(1, len(t)):
            Step = (t[m] - t[m - 1]).total_seconds()
            Soc = Soc - packcrnt1[m] * Step * 100 / (3600 * Capacity)
            Ah = Ah - packcrnt1[m] * Step / 3600
            if (cellvolt2[m] - Volt[-1]) > 0.9 and Ah>0:
                DQ_Ah.append(Ah)
                Volt.append(cellvolt2[m])
                DV_Volt.append(Volt[-1] - Volt[-2])
                DVDQ.append((DV_Volt[-1]) / DQ_Ah[-1])
                Ah = 0
                time1.append(t[m])
                soc1.append(Soc)
                soc2.append(soc[m])
        df_Data1 = pd.DataFrame({'Time': time1,
                                'SOC': soc2,
                                'DVDQ': DVDQ,
                                'AhSOC': soc1})

        df_Data1 = df_Data1[(df_Data1['SOC'] > 50) & (df_Data1['SOC'] < 75)]
        # 寻找峰值点，且峰值点个数>3
        if len(df_Data1['DVDQ'])>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) > 3:
                return df_Data1['AhSOC'][PeakIndex]
            else:
                df_Data1 = df_Data1.drop([PeakIndex])
                PeakIndex = df_Data1['DVDQ'].idxmax()
                return df_Data1['AhSOC'][PeakIndex]


    # 计算最大最小Soc差
    if ChgStartValid:
        DetaSoc2 = []
        DetaSoc=[]
        DetaSoc_SN=[]
        DetaSoc_time=[]
        for i in range(len(ChgStartValid)):
            DetaSoc1 = []
            for j in range(1, CellVoltNums + 1):
                s = str(j)
                cellvolt = df_bms['单体电压' + s]
                cellvolt = list(cellvolt[ChgStartValid[i]:ChgEndValid[i]])
                Time = list(time[ChgStartValid[i]:ChgEndValid[i]])
                Packcrnt = list(packcrnt[ChgStartValid[i]:ChgEndValid[i]])
                SOC1 = list(SOC[ChgStartValid[i]:ChgEndValid[i]])
                a = data_search(Time, SOC1, cellvolt, Packcrnt)
                if a:
                    DetaSoc1.append(a)  # 计算到达峰值点的累计Soc
            if DetaSoc1:
                DetaSoc2.append(max(DetaSoc1) - min(DetaSoc1))

        DetaSocMean = np.mean(DetaSoc2)
        DetaSoc.append(DetaSocMean)
        DetaSoc_SN.append(SNnum)
        DetaSoc_time.append(time[ChgStartValid[-1]])


        result_DetaSoc={'time':DetaSoc_time,
                        'SN号':DetaSoc_SN,
                        'Soc差':DetaSoc}
        Result_DetaSoc=pd.DataFrame(result_DetaSoc)
        print(Result_DetaSoc)