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.

Automated summary

In this article, a multi-timescale capacity and lifespan prediction method is proposed. Capacity prediction is done using long short term memory neural network, while remaining useful life prediction is done using Weibull accelerated failure time regression. The proposed methods are validated using two datasets and have shown high accuracy.