Battery Aging Mechanisms Under Different Fast Charging Protocols: A Comparative Study on State of Health Estimation

An accurate State of Health (SOH) estimation is crucial for ensuring the safe and efficient operation of electric vehicles (EVs). However, accurately estimating SOH in real-world applications is challenging due to diverse aging mechanisms, which result in varying battery characteristics and complicate the development of a universally applicable SOH estimation model. To address this issue, this paper investigates the aging characteristics of four INR21700 Samsung 30T cells subjected to different fast-charging protocols. By analyzing the incremental capacity (IC) curves, we identify specific features that effectively represent the aging status across different aging mechanisms. Utilizing these universal features, we develop a linear regression model (LRM) capable of adapting to various aging mechanisms for SOH estimation. The LRM is trained using data from a single cell and tested on the remaining cells. For the cell with the most similar aging trend, the Mean-Absolute-Error (MAE) is 0.94%, with an R2 value of 0.99. Even for the cell with the most distinct aging trend and mechanism, the MAE is 1.70%, with an R2 value of 0.94. These results demonstrate the robustness and adaptability of the proposed LRM for SOH estimation under diverse aging conditions.