Early prediction of lithium-ion battery degradation with a generative pre-trained transformer

Abstract

The early detection of degradation in lithium-ion batteries (LIBs) is crucial for effective predictive maintenance and recycling. However, accurately predicting the future degradation of LIBs in early stage is challenging due to the barely noticeable performance changes at initial charging cycles and the long-term nonlinear degradation pattern. In this work, we propose a two-stage early-stage degradation prediction method, BatteryGPT, which employs a Generative Pre-trained Transformer (GPT) to autoregressively predict the charging data of entire lifecycle and a state-of-health (SOH) estimator to correlates the predicted charging data with ageing features in LIBs. The validation demonstrates that BatteryGPT can predict the future LIB degradation with high accuracy using early charging data, before any capacity degradation is evident. Predicting with the first 30% of the battery lifetime, BatteryGPT significantly outperforms baselines, achieving a root mean square error (RMSE) of 0.213% for SOH variation prediction, and mean absolute percent errors (MAPE) of 2.30% and 1.18% for knee point and EOL predictions. Even predicting with the first 5% of lifetime charging data, BatteryGPT demonstrates strong early-stage prediction performance.