State-of-health prediction of lithium-ion batteries based on SSA-LSTM network model
Lithium-ion batteries are widely used as power sources for various devices,so rapid and accurate prediction of the SOH(state of health)of lithium-ion batteries is an important means to reduce battery failures.Due to its ability to identify the characteristics,trends and development patterns of variable changes from time series,LSTM(long short-term memory)network is a popular deep learning network method for predicting the future changes of lithium-ion battery SOH.The LSTM method without optimizing hyperparameters can easily lead to low accuracy in battery SOH prediction models.A method based on SSA(sparrow search algorithm)was proposed to optimize LSTM for the prediction of SOH in lithium-ion batteries.A new health indicator-the kurtosis at the peak of the charging voltage PDF(probability density function)curve was extracted and used as input for the SOH prediction model to achieve accurate prediction of battery SOH.The experimental results show that the prediction accuracy of the LSTM model optimized by SSA is better than that of the unoptimized model.When the training set only accounts for 20％of the total data,the root mean square error ERMSE of the NCA(nickel cobalt aluminum)battery SOH prediction results is within 0.7％,and the maximum absolute error is less than 2.0％.SSA-LSTM can accurately predict battery SOH under limited training data.
lithium-ion batterystate of healthsparrow search algorithmlong short-term memory networkhyperparameterkurtosis
万方数据
维普
