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+{
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "from ctypes import Structure\n",
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+ "from re import M\n",
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+ "import pandas as pd\n",
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+ "import numpy as np\n",
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+ "import datetime\n",
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+ "import time, datetime\n",
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+ "import matplotlib.pyplot as plt\n",
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+ "from pylab import*\n",
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+ "from scipy.signal import savgol_filter\n",
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+ "import os\n",
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+ "\n",
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+ "path = r'D:\\Work\\Code_write\\data_analyze_platform\\USER\\lzx\\05问题查询\\PK504B00100004003内阻可疑-211206\\原始数据'\n",
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+ " \n",
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+ "def get_file(): #创建一个空列表\n",
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+ " files =os.listdir(path)\n",
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+ " files.sort() #排序\n",
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+ " file_list= []\n",
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+ " for file in files:\n",
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+ " if not os.path.isdir(path +file): #判断该文件是否是一个文件夹 \n",
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+ " f_name = str(file) \n",
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+ "# print(f_name)\n",
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+ " tr = '\\\\' #多增加一个斜杠\n",
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+ " filename = path + tr + f_name \n",
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+ " file_list.append(filename) \n",
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+ " return file_list \n",
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+ "\n",
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+ "file_list = get_file()\n",
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+ "for file in file_list:\n",
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+ " data = pd.read_csv(file,encoding='GB18030')\n",
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+ " #----------------------------------------定义滤波函数-----------------------------------------------------------------------\n",
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+ " def moving_average(interval, windowsize):\n",
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+ " window = np.ones(int(windowsize)) / float(windowsize)\n",
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+ " re = np.convolve(interval, window, 'same')\n",
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+ " return re\n",
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+ " #----------------------------------------区分充放电数据----------------------------------------------------------------------\n",
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+ " df_soc = data['SOC[%]']\n",
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+ " # df_soc[df_soc >= 99] = 100\n",
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+ " # df_soc_flt = moving_average(df_soc, 3)#SOC滤波\n",
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+ " df_soc_len = len(df_soc)\n",
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+ " df_soc_add = df_soc.iloc[df_soc_len-3:df_soc_len-1]#由于对SOC二次微分,增加两行数据\n",
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+ " df_soc_new = df_soc.append(df_soc_add)\n",
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+ " # df_soc_cal = savgol_filter(df_soc_new, 13, 3)\n",
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+ " df_soc_new_dif = np.diff(df_soc_new,axis=0)#电压一次微分,判断升降\n",
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+ " df_soc_new_dif = pd.DataFrame(df_soc_new_dif)\n",
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+ " df_soc_new_dif[df_soc_new_dif > 0] = 1#SOC升高\n",
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+ " df_soc_new_dif[df_soc_new_dif == 0] = 0#SOC升高或静置\n",
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+ " df_soc_new_dif[df_soc_new_dif < 0] = -1#SOC下降\n",
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+ " df_soc_dif_arr = np.array(df_soc_new_dif)\n",
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+ " pos_change = nonzero(df_soc_dif_arr)#寻找非零元素位置\n",
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+ " value_change = df_soc_dif_arr[pos_change[0]]#非零元素数值\n",
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+ " value_change_dif = np.diff(value_change)#对非零元素值二次微分\n",
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+ " peak_pos = np.where(value_change_dif == -2)#充电结束点\n",
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+ " bot_pos = np.where(value_change_dif == 2)#放电结束点\n",
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+ " # df_soc_new_difdif = np.diff(df_soc_new_dif,axis=0)#三次微分,利用-2,2判断充电-静置与放电-静置的区别\n",
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+ " # df_soc_new_difdif = pd.DataFrame(df_soc_new_difdif)\n",
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+ " # peak_pos = np.where(df_soc_new_difdif == -2)\n",
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+ " # bot_pos = np.where(df_soc_new_difdif == 2)\n",
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+ " print(pos_change)\n",
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+ " # print(df_soc)\n",
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+ " # print(df_soc_new_dif)\n",
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+ " print(value_change)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 23,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "(array([ 6855, 6860, 9481, 9510, 9519, 9538, 9554, 9564, 9618,\n",
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+ " 9637, 9681, 9687, 9694, 9704, 9722, 9752, 9812, 9845,\n",
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+ " 9892, 9916, 9945, 9974, 10004, 10021, 10033, 10053, 10068,\n",
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+ " 10123, 10138, 10150, 10184, 10271, 10374, 10387, 10406, 10449,\n",
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+ " 10459, 10510, 10554, 10567, 10599, 10605], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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+ " dtype=int64))\n",
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+ "[[ 1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]\n",
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+ " [-1]]\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "print(pos_change)\n",
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+ "print(value_change)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "(array([ 6855, 6860, 9481, 9510, 9519, 9538, 9554, 9564, 9618,\n",
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+ " 9637, 9681, 9687, 9694, 9704, 9722, 9752, 9812, 9845,\n",
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+ " 9892, 9916, 9945, 9974, 10004, 10021, 10033, 10053, 10068,\n",
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+ " 10123, 10138, 10150, 10184, 10271, 10374, 10387, 10406, 10449,\n",
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+ " 10459, 10510, 10554, 10567, 10599, 10605], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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+ " dtype=int64))\n",
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+ "[ 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1\n",
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+ " -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1]\n",
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+ "<class 'list'>\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "#-------------------------------------------------------利用soc的升降判断充放电---------------------------------------------------------\n",
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+ "pos_change_dif = np.diff(pos_change[0])#SOC变化时的位置信息\n",
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+ "pos_change_del_temp = np.where(pos_change_dif < 10)\n",
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+ "pos_change_del = pos_change_del_temp[0] + 1\n",
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+ "value_change_new = np.delete(value_change[:,0], pos_change_del)#SOC发生变化数值\n",
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+ "pos_change_new = np.delete(pos_change[0], pos_change_del)#SOC发生变化位置\n",
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+ "print(pos_change)\n",
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+ "print(value_change_new)\n",
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+ "value_change_new_dif = np.diff(value_change_new)#寻找充电或放电结束位置\n",
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+ "value_change_new_dif = np.insert(value_change_new_dif, 0, value_change_new_dif[0])\n",
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+ "chrgr_dischrgr_pos_temp = np.where(abs(value_change_new_dif) == 2)#寻找充电或放电结束位置\n",
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+ "splice_num = []\n",
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+ "if len(chrgr_dischrgr_pos_temp[0]) >= 1:\n",
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+ " chrgr_dischrgr_pos = pos_change_new[chrgr_dischrgr_pos_temp[0]]#原数据中充电或放电结束位置\n",
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+ " chrgr_dischrgr_pos = np.insert(chrgr_dischrgr_pos, 0, 0)\n",
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+ " data_len = len(df_soc)#数据长度\n",
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+ " pos_len = len(chrgr_dischrgr_pos)#切片长度\n",
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+ " chrgr_dischrgr_pos = np.insert(chrgr_dischrgr_pos, pos_len, data_len-1)#\n",
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+ " print(type(splice_num))\n",
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+ " for item in range(0,len(chrgr_dischrgr_pos)-1):\n",
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+ " splice_num.extend(item*np.ones(chrgr_dischrgr_pos[item +1]-chrgr_dischrgr_pos[item]))\n",
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+ " splice_num = np.insert(splice_num, data_len-2, item)\n",
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+ "else:\n",
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+ " splice_num = np.zeros(len(df_soc))\n",
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+ " pos_ful = np.array([0])\n",
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+ "data['stat'] = splice_num"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 27,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "array([ 0, 6855, 9481, 12141], dtype=int64)"
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+ ]
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+ },
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+ "execution_count": 27,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "chrgr_dischrgr_pos"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "#--------------------------------------------------------筛选充电数据与放电数据-------------------------------------\n",
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+ "chrgr_data = pd.DataFrame()\n",
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+ "dischrgr_data = pd.DataFrame()\n",
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+ "for num_chrgr in range(0,len(chrgr_dischrgr_pos)-1):\n",
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+ " df_data_temp = data.loc[(data['stat'] == num_chrgr)]#筛选每段数据\n",
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+ " data_splice_len = len(df_data_temp)\n",
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+ " if np.count_nonzero(df_data_temp['总电流[A]'] > 0) > 0.3*data_splice_len:\n",
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+ " chrgr_data_temp = df_data_temp.loc[df_data_temp['总电流[A]'] >= 0]#筛选具有充电电流的充电数据\n",
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+ " chrgr_data = chrgr_data.append(chrgr_data_temp)\n",
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+ " chrgr_data = chrgr_data.reset_index(drop = True)\n",
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+ " else:\n",
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+ " dischrgr_data_temp = df_data_temp.loc[df_data_temp['总电流[A]'] <= 0]#筛选具有放电电流的放电数据\n",
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+ " dischrgr_data = dischrgr_data.append(dischrgr_data_temp)\n",
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+ " dischrgr_data = dischrgr_data.reset_index(drop = True)\n",
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+ "chrgr_num_difference = np.unique(chrgr_data['stat'])"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 5,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ " Unnamed: 0 时间戳 GSM信号 故障等级 故障代码 总电流[A] 总电压[V] \\\n",
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+ "9481 9481 2021-12-02 20:29:00 19 0 0 0.2 65.5 \n",
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+ "9483 9483 2021-12-02 20:29:44 30 0 0 0.0 65.6 \n",
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+ "9484 9484 2021-12-02 20:29:54 30 0 0 0.6 65.6 \n",
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+ "9485 9485 2021-12-02 20:30:03 31 0 0 0.5 65.6 \n",
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+ "9486 9486 2021-12-02 20:30:13 31 0 0 0.4 65.7 \n",
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+ "... ... ... ... ... ... ... ... \n",
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+ "12137 12137 2021-12-03 23:55:59 23 0 0 0.0 65.6 \n",
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+ "12138 12138 2021-12-03 23:56:59 23 0 0 0.0 65.6 \n",
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+ "12139 12139 2021-12-03 23:57:59 23 0 0 0.0 65.6 \n",
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+ "12140 12140 2021-12-03 23:58:59 23 0 0 0.0 65.6 \n",
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+ "12141 12141 2021-12-03 23:59:59 23 0 0 0.0 65.5 \n",
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+ "\n",
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+ " 外电压 总输出状态 上锁状态 ... 单体温度1 单体温度2 单体温度3 单体温度4 其他温度1 其他温度2 其他温度3 \\\n",
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+ "9481 0 3 0 ... 13 14 14 14 16 15 13 \n",
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+ "9483 0 3 0 ... 14 14 14 13 18 16 13 \n",
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+ "9484 0 3 0 ... 14 14 14 14 18 16 13 \n",
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+ "9485 0 3 0 ... 14 14 14 14 18 16 13 \n",
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+ "9486 0 3 0 ... 13 14 14 14 18 16 13 \n",
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+ "... ... ... ... ... ... ... ... ... ... ... ... \n",
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+ "12137 0 3 0 ... 14 14 14 14 15 15 13 \n",
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+ "12138 0 3 0 ... 14 14 14 14 15 15 13 \n",
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+ "12139 0 3 0 ... 14 14 14 14 15 15 13 \n",
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+ "12140 0 3 0 ... 14 14 14 14 15 15 13 \n",
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+ "12141 0 3 0 ... 14 14 14 14 15 15 13 \n",
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+ "\n",
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+ " 其他温度4 其他温度5 stat \n",
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+ "9481 15 14 2.0 \n",
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+ "9483 15 13 2.0 \n",
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+ "9484 15 14 2.0 \n",
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+ "9485 15 14 2.0 \n",
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+ "9486 15 14 2.0 \n",
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+ "... ... ... ... \n",
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+ "12137 15 14 2.0 \n",
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+ "12138 15 14 2.0 \n",
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+ "12139 15 14 2.0 \n",
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+ "12140 15 14 2.0 \n",
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+ "12141 15 14 2.0 \n",
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+ "\n",
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+ "[2590 rows x 47 columns]\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "print(chrgr_data_temp)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 7,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "LookTab_SOC = [0.00, \t2.40, \t6.38, \t10.37, \t14.35, \t18.33, \t22.32, \t26.30, \t30.28, \t35.26, \t40.24, \t45.22, \t50.20, \t54.19, \t58.17, \t60.16, \t65.14, \t70.12, \t75.10, \t80.08, \t84.06, \t88.05, \t92.03, \t96.02, \t100.00, 105]\n",
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+ "LookTab_OCV = [2.7151,\t3.0298,\t3.1935,\t3.2009,\t3.2167,\t3.2393,\t3.2561,\t3.2703,\t3.2843,\t3.2871,\t3.2874,\t3.2868,\t3.2896,\t3.2917,\t3.2967,\t3.3128,\t3.3283,\t3.3286,\t3.3287,\t3.3288,\t3.3289,\t3.3296,\t3.3302,\t3.3314,\t3.3429, 3.6]"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 16,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
|
|
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+ "output_type": "stream",
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+ "text": [
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+ " 单体电压1 单体电压2 单体电压3 单体电压4 单体电压5 单体电压6 单体电压7 单体电压8 单体电压9 单体电压10 \\\n",
|
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+ "787 3.281 3.286 3.283 3.284 3.283 3.283 3.286 3.286 3.287 3.285 \n",
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+ "\n",
|
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+ " 单体电压11 单体电压12 单体电压13 单体电压14 单体电压15 单体电压16 单体电压17 单体电压18 单体电压19 \\\n",
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+ "787 3.282 3.288 3.287 3.284 3.284 3.286 3.289 3.289 3.288 \n",
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+ "\n",
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+ " 单体电压20 \n",
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+ "787 3.287 \n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "#--------------------------------------------------------统计充电截止电压、充电SOC区间---------------------------------------------------------\n",
|
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+ "from scipy import interpolate\n",
|
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+ "cell_name = ['单体电压'+str(x) for x in range(1,21)]#电芯电压名称\n",
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+ "chrgr_volt = pd.DataFrame()\n",
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+ "chrgr_fin_volt = pd.DataFrame()\n",
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+ "chrgr_soc = pd.DataFrame()\n",
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+ "f = interpolate.interp1d(LookTab_OCV, LookTab_SOC, kind = 'linear')#差值函数\n",
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+ "for chrgr_num in chrgr_num_difference:\n",
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+ " data_temp = chrgr_data.loc[chrgr_data['stat'] == chrgr_num]#该片段数据\n",
|
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+ " chrgr_crnt_data = data_temp.loc[data_temp['总电流[A]'] > 0]#充电数据中有电流部分,充电时电流为正\n",
|
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+ " chrgr_crnt_data = chrgr_crnt_data.reset_index(drop = True)\n",
|
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+ " chrgr_crnt_zero_before = data_temp.loc[data_temp['时间戳'] < chrgr_crnt_data['时间戳'][0]]#充电数据中出现电流前数据\n",
|
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+ " chrgr_crnt_zero_after = data_temp.loc[data_temp['时间戳'] > chrgr_crnt_data.iloc[-1]['时间戳']]#充电数据中出现电流前数据\n",
|
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+ " chrgr_crnt_zero_before = chrgr_crnt_zero_before.reset_index(drop = True)\n",
|
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+ " chrgr_crnt_zero_after = chrgr_crnt_zero_after.reset_index(drop = True)\n",
|
|
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+ " time_before = pd.to_datetime(chrgr_crnt_zero_before['时间戳'])\n",
|
|
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+ " time_after = pd.to_datetime(chrgr_crnt_zero_after['时间戳'])\n",
|
|
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+ " chrgr_rest_len = len(time_after)\n",
|
|
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+ " if chrgr_rest_len > 1:\n",
|
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+ " if (time_after[chrgr_rest_len - 1] - time_after[0])/pd.Timedelta(1, 'min') > 10:\n",
|
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+ " # chrgr_volt = chrgr_volt.append(chrgr_crnt_zero_before.iloc[-1])#充电开始前静置电压\n",
|
|
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+ " chrgr_volt = chrgr_volt.append(chrgr_crnt_zero_after.iloc[-1])#充电结束静置电压\n",
|
|
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+ " df_cell_volt = chrgr_volt[cell_name]/1000\n",
|
|
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+ " df_cell_volt[df_cell_volt >= 3.6] = 3.595\n",
|
|
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+ " df_cell_volt[df_cell_volt <= 2.715] = 2.72\n",
|
|
|
+ " print(df_cell_volt)\n",
|
|
|
+ " chrgr_soc = f(df_cell_volt)\n",
|
|
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+ "# for cell_num in cell_name:#获取各个电芯充电结束后的SOC\n",
|
|
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+ "# chrgr_cellsoc = f(chrgr_volt[cell_num]/1000)\n",
|
|
|
+ "# chrgr_cellsoc_temp = pd.DataFrame(chrgr_cellsoc)\n",
|
|
|
+ "# chrgr_soc = pd.concat([chrgr_soc, chrgr_cellsoc_temp], axis = 1)#按列合并各个电芯\n",
|
|
|
+ "# chrgr_soc = chrgr_soc.reset_index(drop = True)\n",
|
|
|
+ "# chrgr_soc.columns = cell_name\n",
|
|
|
+ "# chrgr_soc['时间戳'] = list(chrgr_volt['时间戳'])\n",
|
|
|
+ "# chrgr_soc['stat'] = list(chrgr_volt['stat'])\n",
|
|
|
+ "# chrgr_soc['soc_delta'] = np.max(chrgr_soc[cell_name], axis = 1) - np.min(chrgr_soc[cell_name], axis = 1)\n",
|
|
|
+ "# chrgr_soc['sn'] = 'sn'\n",
|
|
|
+ "# chrgr_soc.to_csv(r'D:\\Work\\Code_write\\data_analyze_platform\\USER\\lzx\\05问题查询\\PK504B00100004003内阻可疑-211206\\充电行为统计\\充电截止SOC.csv',index=False,encoding='GB18030')"
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": 19,
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [
|
|
|
+ {
|
|
|
+ "name": "stdout",
|
|
|
+ "output_type": "stream",
|
|
|
+ "text": [
|
|
|
+ "0 0.0\n",
|
|
|
+ "1 0.0\n",
|
|
|
+ "2 0.0\n",
|
|
|
+ "3 0.0\n",
|
|
|
+ "4 0.0\n",
|
|
|
+ " ... \n",
|
|
|
+ "12137 2.0\n",
|
|
|
+ "12138 2.0\n",
|
|
|
+ "12139 2.0\n",
|
|
|
+ "12140 2.0\n",
|
|
|
+ "12141 2.0\n",
|
|
|
+ "Name: stat, Length: 12142, dtype: float64\n"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "print(data['stat'])"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "metadata": {
|
|
|
+ "interpreter": {
|
|
|
+ "hash": "b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f"
|
|
|
+ },
|
|
|
+ "kernelspec": {
|
|
|
+ "display_name": "Python 3.8.8 64-bit ('base': conda)",
|
|
|
+ "language": "python",
|
|
|
+ "name": "python3"
|
|
|
+ },
|
|
|
+ "language_info": {
|
|
|
+ "codemirror_mode": {
|
|
|
+ "name": "ipython",
|
|
|
+ "version": 3
|
|
|
+ },
|
|
|
+ "file_extension": ".py",
|
|
|
+ "mimetype": "text/x-python",
|
|
|
+ "name": "python",
|
|
|
+ "nbconvert_exporter": "python",
|
|
|
+ "pygments_lexer": "ipython3",
|
|
|
+ "version": "3.8.8"
|
|
|
+ },
|
|
|
+ "orig_nbformat": 4
|
|
|
+ },
|
|
|
+ "nbformat": 4,
|
|
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+ "nbformat_minor": 2
|
|
|
+}
|