A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries
Author:
Han Chao12,
Gao Yu‐Chen2,
Chen Xiang2,
Liu Xinyan3,
Yao Nan2,
Yu Legeng1,
Kong Long1,
Zhang Qiang2ORCID
Affiliation:
1. Frontiers Science Center for Flexible Electronics and Xi'an Institute of Flexible Electronics (IFE) Northwestern Polytechnical University Xi'an the People's Republic of China
2. Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering Tsinghua University Beijing the People's Republic of China
3. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China Chengdu Sichuan the People's Republic of China
Abstract
AbstractAccurately forecasting the nonlinear degradation of lithium‐ion batteries (LIBs) using early‐cycle data can obviously shorten the battery test time, which accelerates battery optimization and production. In this work, a self‐adaptive long short‐term memory (SA‐LSTM) method has been proposed to predict the battery degradation trajectory and battery lifespan with only early cycling data. Specifically, two features were extracted from discharge voltage curves by a time‐series‐based approach and forecasted to further cycles using SA‐LSTM model. The as‐obtained features were correlated with the capacity to predict the capacity degradation trajectory by generalized multiple linear regression model. The proposed method achieved an average online prediction error of 6.00% and 6.74% for discharge capacity and end of life, respectively, when using the early‐cycle discharge information until 90% capacity retention. Furthermore, the importance of temperature control was highlighted by correlating the features with the average temperature in each cycle. This work develops a self‐adaptive data‐driven method to accurately predict the cycling life of LIBs, and unveils the underlying degradation mechanism and the importance of controlling environmental temperature.image
Funder
National Key Research and Development Program of China
Natural Science Foundation of Beijing Municipality
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Subject
Materials Chemistry,Surfaces, Coatings and Films,Materials Science (miscellaneous),Electronic, Optical and Magnetic Materials