A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries

Abstract

Abstract Accurately 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.

Keywords

• cycling lifespan prediction
• lithium‐ion batteries
• long short‐term memory method
• machine learning
• time series forecasting