A novel method of prediction for capacity and remaining useful life of lithium-ion battery based on multi-time scale Weibull accelerated failure time regression

Lithium-ion batteries are essential energy storage components for electrical grid, and the health diagnosis de-termines the safety of the battery during usage and the rational classify of echelon utilization. In this article, a multi-timescale capacity and lifespan prediction method is proposed where capacity prediction and remaining useful life prediction are divided into the short-time scale and the long-time scale. For capacity prediction, the long short term memory neural network with five significant features is applied according to its accuracy per-formance in time series prediction. As for remaining useful life, the Weibull accelerated failure time regression is proposed to improve the prediction efficiency of a large amount of data. Finally, the predictive capability, robustness and effectiveness of proposed methods are verified using two datasets with different cycling test conditions within an error of 3.9 % in long-time scale and 2.7 % in short-time scale. The proposed method has great potential for targeted and accurate health state forecasting and long-term end of life prediction.