A transfer learning-based ensemble learning model for electric vehicles lithium-ion battery capacity estimation using electrochemical impedance spectroscopy

Lithium-ion battery capacity estimation is crucial to ensure the operational reliability and safety of electric vehicles. Electrochemical Impedance Spectroscopy (EIS) can provide rich physical degradation information of the battery, which makes the EIS-based data-driven method a promising solution for accurate battery capacity estimation. However, batteries tend to present diverse degradation patterns due to the diverse operating conditions and manufacturing, resulting in large capacity estimation errors in practical applications. Therefore, this paper proposes a transfer learning (TL) based ensemble learning (EL) method based on multi-layer perception for the battery capacity estimation. First, the influential impedance features are identified from the EIS using Spearman rank coefficient analysis. Second, three TL models are developed based on distinct TL strategies to enable sufficient knowledge transfer considering the different degradation pattern. Further, an EL method with Gaussian kernel-based weighted assignment technique is proposed to integrate the developed TL models and improve the estimation robustness. The performance of the developed model has been successfully validated only using the first 1/3 cycles of the historical data from target battery obtained under different operating conditions and batteries. The results reveal that the proposed method can largely suppress estimation errors, presenting more accurate capacity estimation compared to other methods with an RMSE lower than 1%.