Capacity estimation of Li-ion battery based on transformer-adversarial discriminative domain adaptation

Abstract

Lithium-ion batteries are widely used in various electronic devices as well as electric vehicles, and accurate estimation of the battery capacity is important to ensure safe and reliable operation of the system. However, in practice, the complex working conditions and the limitation of the number of charge/discharge cycles lead to insufficient historical data and inaccurate capacity estimation. In order to improve the adaptability as well as accuracy under different operating conditions, this paper proposes a lithium-ion battery capacity estimation model based on Transformer-Adversarial Discriminative Domain Adaptation (T-ADDA). The model takes charging voltage, charging current, and charging temperature as inputs and uses a transformer network to extract the time series features from the data. Then, adversarial domain adaptation is trained on the source and target domain data by the domain discrimination network of the ADDA model so as to find the domain invariant features between the source and target domains. Finally, the regression network of ADDA is used to achieve cross-domain prediction for the target domain data. The experimental results show that the T-ADDA model can accurately achieve cross-domain prediction and that the average error of prediction under different operating conditions is only 3.9225%. Therefore, the T-ADDA model has good adaptability and accuracy, and it can significantly improve the performance of lithium-ion battery capacity estimation.