磷酸铁锂一致性代码

LIB/MIDDLE/DetaSoc/LFPDeltSoc.py

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+# 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)
+

LIB/MIDDLE/DetaSoc/detaSOC表头及数据类型.xlsx

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+±íÍ·	Ãû³Æ	Êý¾ÝÀàÐÍ
+time	time	timestamps
+SNºÅ	sn	str
+Soc²î	deta_soc	float64