feat: 添加RUL-Mamba模型及相关组件
新增锂电池剩余使用寿命预测模型RUL-Mamba，包含以下主要组件： 1. 添加Mamba模块作为核心时序建模组件 2. 实现特征注意力网络(FAN)和门控残差网络(GRN) 3. 新增数据预处理和归一化层 4. 添加模型训练和评估脚本 5. 补充README文档说明使用方法 6. 添加可视化辅助工具Helper_Plot.py 7. 实现多种时间序列处理层(Embedding、AutoCorrelation等) 8. 添加配置文件requirements.txt 9. 补充测试数据集TJU battery data
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import numpy as np
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import pandas as pd
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import argparse
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import glob
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import os
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import torch
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from torch.utils.data import TensorDataset
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from torch.utils.data import DataLoader
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from assistant import get_gpus_memory_info
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from sklearn.model_selection import train_test_split
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id,_ = get_gpus_memory_info()
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os.environ["CUDA_VISIBLE_DEVICES"] = str(id)
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def write_to_txt(txt_name,txt):
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with open(txt_name,'a') as f:
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f.write(txt)
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f.write('\n')
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class DF():
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def __init__(self,args):
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self.args = args
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def _3_sigma(self, Ser1):
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'''
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:param Ser1:
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:return: index
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'''
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rule = (Ser1.mean() - 3 * Ser1.std() > Ser1) | (Ser1.mean() + 3 * Ser1.std() < Ser1)
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index = np.arange(Ser1.shape[0])[rule]
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return index
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def delete_3_sigma(self,df):
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'''
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:param df: DataFrame
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:return: DataFrame
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'''
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df = df.replace([np.inf, -np.inf], np.nan)
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df = df.dropna()
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df = df.reset_index(drop=True)
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out_index = []
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for col in df.columns:
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index = self._3_sigma(df[col])
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out_index.extend(index)
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out_index = list(set(out_index))
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df = df.drop(out_index, axis=0)
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df = df.reset_index(drop=True)
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df['Cycle'] = df.index + 1
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return df
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def read_one_csv(self,file_name):
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'''
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read a csv file and return a DataFrame
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:param file_name: str
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:return: DataFrame
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'''
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df = pd.read_csv(file_name)
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df.insert(df.shape[1]-1,'cycle index',np.arange(df.shape[0]))
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df = self.delete_3_sigma(df)
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return df
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def BatteryDataRead(args):
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root = 'data/TJU data/Dataset_3_NCM_NCA_battery/'
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files = os.listdir(root)
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Battery_list = {}
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for i in range(1,4):
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for f in files:
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if 'CY25-05_1' in f and '#'+str(i) in f:
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path = os.path.join(root,f)
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df_i = DF(args)
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df = df_i.read_one_csv(path)
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df = df.rename(columns={'capacity': 'Capacity'})
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df['BatteryName'] = f'CY25_{i}'
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Battery_list[f'CY25_{i}'] = df
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return Battery_list
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def MultiVariateBatteryDataProcess(BatteryData,test_name,start_point,args):
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dict_without_test = {key: value for key, value in BatteryData.items() if key != test_name}
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df_train = pd.concat(dict_without_test.values())
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df_train = df_train.filter(items=['BatteryName', 'Cycle','voltage mean','voltage std','voltage kurtosis','voltage skewness','CC Q','CC charge time','voltage slope','voltage entropy',
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'current mean','current std','current kurtosis','current skewness','CV Q','CV charge time','current slope','current entropy','Capacity'])
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df_train['Capacity'] /= args.Rated_Capacity
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df_train['target'] = df_train['Capacity']
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df_train['time_idx'] = df_train['Cycle'].map(lambda x: int(x-1))
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df_train['group_id'] = df_train['BatteryName'].map({'CY25_1':0, 'CY25_2':1, 'CY25_3':2})
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df_train = df_train.drop(['BatteryName'],axis=1)
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df_train['idx'] = [x for x in range(len(df_train))]
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df_train.set_index('idx',inplace=True)
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df_test = BatteryData[test_name]
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df_test = df_test.filter(items=['BatteryName', 'Cycle','voltage mean','voltage std','voltage kurtosis','voltage skewness','CC Q','CC charge time','voltage slope','voltage entropy',
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'current mean','current std','current kurtosis','current skewness','CV Q','CV charge time','current slope','current entropy','Capacity'])
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df_test['Capacity'] /= args.Rated_Capacity
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df_test['target'] = df_test['Capacity']
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df_test['time_idx'] = df_test['Cycle'].map(lambda x: int(x-1))
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df_test['group_id'] = df_test['BatteryName'].map({'CY25_1':0, 'CY25_2':1, 'CY25_3':2})
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df_test = df_test.drop(['BatteryName'],axis=1)
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df_test['idx'] = [x for x in range(len(df_test))]
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df_test.set_index('idx',inplace=True)
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min_val = df_train['Capacity'].min()
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max_val = df_train['Capacity'].max()
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df_train['Capacity'] = (df_train['Capacity']-min_val) / (max_val - min_val)
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df_test['Capacity'] = (df_test['Capacity']-min_val) / (max_val - min_val)
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#测试集全部数据
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df_all = df_test
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#测试集参与测试的数据
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df_test = df_all.loc[df_all['Cycle']>=start_point-args.seq_len,['time_idx','group_id','Cycle','voltage mean','voltage std','voltage kurtosis','voltage skewness','CC Q','CC charge time','voltage slope','voltage entropy',
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'current mean','current std','current kurtosis','current skewness','CV Q','CV charge time','current slope','current entropy','Capacity','target']]
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df_test['idx'] = [x for x in range(len(df_test))]
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df_test.set_index('idx',inplace=True)
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return df_train,df_test,df_all
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def BatteryDataProcess(BatteryData,test_name,start_point):
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dict_without_test = {key: value for key, value in BatteryData.items() if key != test_name}
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df_train = pd.concat(dict_without_test.values())
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df_train = df_train.filter(items=['BatteryName', 'Cycle','Capacity'])
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df_train['Capacity'] /= args.Rated_Capacity
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df_train['target'] = df_train['Capacity']
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df_train['time_idx'] = df_train['Cycle'].map(lambda x: int(x-1))
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df_train['group_id'] = df_train['BatteryName'].map({'CY25_1':0, 'CY25_2':1, 'CY25_3':2})
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df_train = df_train.drop(['BatteryName'],axis=1)
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df_train['idx'] = [x for x in range(len(df_train))]
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df_train.set_index('idx',inplace=True)
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df_test = BatteryData[test_name]
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df_test = df_test.filter(items=['BatteryName', 'Cycle','Capacity'])
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df_test['Capacity'] /= args.Rated_Capacity
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df_test['target'] = df_test['Capacity']
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df_test['time_idx'] = df_test['Cycle'].map(lambda x: int(x-1))
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df_test['group_id'] = df_test['BatteryName'].map({'CY25_1':0, 'CY25_2':1, 'CY25_3':2})
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df_test = df_test.drop(['BatteryName'],axis=1)
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df_test['idx'] = [x for x in range(len(df_test))]
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df_test.set_index('idx',inplace=True)
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min_val = df_train['Capacity'].min()
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max_val = df_train['Capacity'].max()
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df_train['Capacity'] = (df_train['Capacity']-min_val) / (max_val - min_val)
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df_test['Capacity'] = (df_test['Capacity']-min_val) / (max_val - min_val)
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#测试集全部数据
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df_all = df_test
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#测试集参与测试的数据
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df_test = df_all.loc[df_all['Cycle']>=start_point-args.seq_len,['time_idx','group_id','Cycle','Capacity','target']]
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df_test['idx'] = [x for x in range(len(df_test))]
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df_test.set_index('idx',inplace=True)
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return df_train,df_test,df_all
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--seq_len', type=int, default=64, help='input sequence length')
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parser.add_argument('--label_len', type=int, default=0, help='start token length')
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parser.add_argument('--pred_len', type=int, default=1, help='prediction sequence length')
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parser.add_argument('--Battery_list', type=list, default=['CY25_1', 'CY25_2', 'CY25_3'], help='Battery data')
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parser.add_argument('--data_dir', type=str, default='data/TJU data/Dataset_3_NCM_NCA_battery/', help='path of the data file')
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parser.add_argument('--Rated_Capacity', type=float, default=2.5, help='Rate Capacity')
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parser.add_argument('--test_name', type=str, default='CY25_1', help='Battery data used for test')
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parser.add_argument('--start_point_list', type=int, default=[200,300,400], help='The cycle when prediction gets started.')
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args = parser.parse_args()
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BatteryData = BatteryDataRead(args)
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BatteryData_array = np.array([BatteryData], dtype=object)
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np.save('data/TJU data/Dataset_3_NCM_NCA_battery_1C.npy', BatteryData_array, allow_pickle=True)
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BatteryData = np.load('data/TJU data/Dataset_3_NCM_NCA_battery_1C.npy', allow_pickle=True)
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BatteryData = BatteryData.item()
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_,_,df_all = MultiVariateBatteryDataProcess(BatteryData,args.test_name,args.start_point_list[0],args)
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real_data = df_all['target'].values*args.Rated_Capacity
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if not os.path.exists('results'):
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os.makedirs('results')
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torch.save(real_data, 'results/Capacity_{}_real_data.pth'.format(args.test_name))
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