Confidence-aware quantile Transformer for reliable degradation prediction of battery energy storage systems

Abstract

Battery energy storage systems (BESS) play a vital role in grid stabilization, integrating renewable energy, and enhancing resilience through efficient energy storage and distribution. Precisely predicting the BESS degradation status is paramount for timely maintenance, ensuring safety, and upholding reliability. The degradation process of batteries in BESS involves complex chemical reactions and physical changes, compounded by various uncertain factors such as diverse battery usage conditions. To address this challenge, the quantile Transformer (Q-Transformer) method is proposed, which can predict the degradation of batteries within intervals, thereby enhancing the reliability of predictions. Firstly, the voltage and current data of lithium-ion batteries in BESS are converted into capacity increment features through incremental capacity analysis. Subsequently, the constructed Q-Transformer model is trained using these capacity increment features. Finally, the trained Q-Transformer model is employed to predict the capacity of lithium-ion batteries in BESS. The effectiveness of the proposed Q-Transformer method is validated and analyzed using two lithium-ion battery datasets, NASA and CALCE. The experimental results indicate that the proposed Q-Transformer method exhibits superior predictive performance than other popular methods. The errors in terms of RMSE, MAPE, and MD-MAPE are mostly within about 5%. The proposed Q-Transformer method shows promising potential for extensive application in BESS.