Energy Storage Battery Life Prediction Based on CSA-BiLSTM

Life prediction of energy storage battery is very important for new energy station. With the increase of using times, energy storage lithium-ion battery will gradually age. Aging of energy storage lithium-ion battery is a long-term nonlinear process. In order to improve the prediction of SOH of energy storage lithium-ion battery, a prediction model combining chameleon optimization and bidirectional Long Short-Term Memory neural network (CSA-BiLSTM) was proposed in this paper. The maximum discharge capacity of the battery was used to define the battery SOH. The chameleon optimization algorithm was introduced into the architecture of the bidirectional short-short memory network to optimize the network. The percentages of MAE and RMSE were 2.501 and 2.511% before optimization, and 1.292 and 1.420% after optimization, respectively. The optimized model has high prediction accuracy.