Lithium-ion Battery Health State Estimation Based on LSTM Neural Network
Battery state of health(SOH)is an important parameter to characterize battery performance,and accurate SOH estimation is important for battery management and maintenance.The aim of this study is to build a battery SOH estimation model using a long-short-term memory(LSTM)neural network,and to obtain the best model accuracy under different iteration numbers.In this paper,real-time battery operation data are first collected and cleaned and filtered.Then,constant-current charging time,constant-voltage charging time and average discharge voltage are selected as feature indicators to predict the battery health state.By comparing and analyzing the real and predicted values of the three batteries,and the values of mean absolute percentage error(MAPE),root mean square error(RMSE),mean absolute error(MAE)and relative error(RE)evaluation indexes,the accuracy of the three battery models is obtained to be above 98%.The experimental results show that the SOH estimation algorithm based on LSTM has accuracy and feasibility.
lithium-ion batterySpearman rank correlation coefficientbattery health statusLSTM neural network
万方数据
维普
