Transfer Learning-Based Hybrid Remaining Useful Life Prediction for Lithium-Ion Batteries Under Different Stresses

Abstract

Accurate remaining useful life (RUL) prediction of lithium-ion battery is very important for the safety of power system of mobile equipment. At present, most of the currently proposed RUL prediction methods for lithium-ion batteries are limited by the length of the training data, and the RUL is also difficult to predict when the predicted dataset is different from the training dataset in terms of the mapping of operating conditions, and the results obtained are difficult to achieve the desired accuracy. To solve this problem, in this work, a new model is proposed. It is a fusion of the long short-term memory (LSTM) network model based on transfer learning and particle filter (PF) model. LSTM based on transfer learning (TL-LSTM) is used for long-term prediction of the lithium-ion battery capacity, which makes the model have the generalization ability to deal with the RUL prediction under different stress. Since LSTM predicts RUL as a single-point prediction, we introduce a PF model to increase the uncertainty representation of RUL prediction. The predicted value from TL-LSTM model is used as observation for the PF prediction model, and the predicted value is continuously adjusted and updated during the iterative process to achieve the uncertainty representation and improve accuracy. The model is validated with the actual tested battery cycle dataset. The experimental results show that the method is effective for the RUL prediction problem of the mapping under different working conditions, and the relative error is less than 5% when the data length is 50%, which proves the application potential of the method.