Research on the Remaining Useful Life Prediction Method of Energy Storage Battery Based on Multimodel Integration

The remaining useful life (RUL) of lithium-ion batteries (LIBs) needs to be accurately predicted to enhance equipment safety and battery management system design. Currently, a single machine learning approach (including an improved machine learning approach) has poor generalization performance due to stochasticity, and the combined prediction approach lacks sufficient theoretical support at the same time. In this paper, we first analyze the prediction principles and applicability of models such as long and short-term memory networks and random forests, and then propose a method for predicting the RUL of batteries based on the integration of multiple-model, and finally validate the proposed model by using experimental data. The experimental results show that (1) for the proposed model, in the best case, the root-mean-square error (RMSE) does not exceed 0.14%, which has a stronger generalization; (2) for the comparison with the single model used, the average RMSE is reduced by 46.2%, 43.7%, and 80.6%, which has a better fitting performance. These results show that the model has good prediction accuracy and application prospects for predicting the RUL of energy storage batteries.