Early prediction of remaining useful life for Lithium-ion batteries based on a hybrid machine learning method

Early prediction of battery remaining useful life (RUL) is critical to ensure a steady energy supply and the effective usage of energy. To reduce the amount of degradation data required for accurate RUL prediction, this study proposed a deep-learning-based prediction algorithm, namely ADLSTM-MC, which combines the features of adaptive dropout long short-term memory (ADLSTM) and Monte Carlo (MC) simulation. The adaptive dropout method was optimized by the Bayesian optimization. MC simulation is combined to describe the uncertainties of prediction results. To evaluate the performance of the proposed method, we developed two data sets including LiFePO4/graphite and LiNi0.8Co0.15Al0.05O2/graphite batteries with different charge/discharge rates and discharge cut-off voltages. The ADLSTM-MC method is shown to precisely achieve early prediction with only 25% of the entire degradation data, while existing models typically require 40%-70% of the degradation data. The R2 of the proposed capacity prediction model is up to 0.957-0.982, and the prediction errors including root mean square error (RMSE) and mean absolute error (MAE) are less than 0.033 and 0.027, respectively. The results show that the proposed method outperforms the mainstream regression algorithms and several recent published hybrid methods in terms of data requirement and prediction accuracy.

Automated summary

A deep-learning-based prediction algorithm, ADLSTM-MC, was proposed for early prediction of battery remaining useful life, which outperforms existing models in terms of accuracy and data requirement.