基于机器学习的锂电池RUL SOH预测

数据集为NASA锂电池数据集。

import datetimeimport numpy as npimport pandas as pdfrom scipy.io import loadmatfrom sklearn.preprocessing import MinMaxScalerfrom sklearn.metrics import mean_squared_errorfrom sklearn import metricsimport matplotlib.pyplot as pltimport seaborn as sns

Load Dataset

def load_data(battery):  mat = loadmat('' + battery + '.mat')  print('Total data in dataset: ', len(mat[battery][0, 0]['cycle'][0]))  counter = 0  dataset = []  capacity_data = []    for i in range(len(mat[battery][0, 0]['cycle'][0])):    row = mat[battery][0, 0]['cycle'][0, i]    if row['type'][0] == 'discharge':      ambient_temperature = row['ambient_temperature'][0][0]      date_time = datetime.datetime(int(row['time'][0][0]),                               int(row['time'][0][1]),                               int(row['time'][0][2]),                               int(row['time'][0][3]),                               int(row['time'][0][4])) + datetime.timedelta(seconds=int(row['time'][0][5]))      data = row['data']      capacity = data[0][0]['Capacity'][0][0]      for j in range(len(data[0][0]['Voltage_measured'][0])):        voltage_measured = data[0][0]['Voltage_measured'][0][j]        current_measured = data[0][0]['Current_measured'][0][j]        temperature_measured = data[0][0]['Temperature_measured'][0][j]        current_load = data[0][0]['Current_load'][0][j]        voltage_load = data[0][0]['Voltage_load'][0][j]        time = data[0][0]['Time'][0][j]        dataset.append([counter + 1, ambient_temperature, date_time, capacity,                        voltage_measured, current_measured,                        temperature_measured, current_load,                        voltage_load, time])      capacity_data.append([counter + 1, ambient_temperature, date_time, capacity])      counter = counter + 1  print(dataset[0])  return [pd.DataFrame(data=dataset,                       columns=['cycle', 'ambient_temperature', 'datetime',                                'capacity', 'voltage_measured',                                'current_measured', 'temperature_measured',                                'current_load', 'voltage_load', 'time']),          pd.DataFrame(data=capacity_data,                       columns=['cycle', 'ambient_temperature', 'datetime',                                'capacity'])]dataset, capacity = load_data('B0005')