Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning
Danpeng Cheng, Wuxin Sha, Linna Wang, Shun Tang, Aijun Ma, Yongwei Chen, Huawei Wang, Ping Lou, Songfeng Lu, Yuan‐Cheng Cao
Abstract
Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries.