Lithium Battery State of Health Assessment Method Based on Bayesian Optimization LSTM
The State of Health(SoH)of lithium-ion batteries is one of the main indicators of battery management systems.In order to improve the accuracy of SoH assessment of lithium batteries,a new BO-LSTM neural network is formed by combining Bayesian Optimization(BO)algorithm,which can solve the difficulty of human experience parame-ter tuning,and Long Short Term Memory(LSTM)neural network with long-term memory ability.Selecting the publicly available lithium battery dataset from NASA for validation,the results showed that compared to a single BP neural net-work model and a single LSTM neural network model,the BO-LSTM neural network has higher accuracy in assessing the SoH of lithium batteries,with improvements of 14.3%and 15.3%on battery B0005 and 23.8%and 20.5%on battery B0006,respectively.This indicates that the LSTM neural network based on Bayesian optimization has better performance in assessing the SoH of lithium batteries and has higher value in practical applications.
lithium batteryBayesian optimization algorithmlong short-term memory neural networkstate of healthassessment