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

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

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