In electric vehicles (EVs), the battery management system is critical for ensuring both performance and safety by monitoring temperature, voltage, and current. However, these indirect indicators cannot fully represent the complex electrochemical and physical processes inside the battery, limiting the accuracy of state‐of‐health (SOH) estimation. Electrochemical impedance spectroscopy (EIS) provides a more direct assessment by analyzing frequency responses, but its real‐time application in onboard systems is constrained by computational complexity and costly additional equipment. To address these challenges, this study proposes an SOH estimation algorithm that integrates electrical and electrochemical information through alternating current (AC) signal conversion and a probability density‐based fusion model. First, direct current (DC) signals are converted into AC, enabling the extraction of internal impedance variations and facilitating real‐time SOH estimation. Second, parameters derived from AC signals are used to build an EIS model, which is then fused with conventional DC models via a weighted probability density approach. Third, a partial capacity‐based SOH estimation method is implemented to further enhance diagnostic accuracy. The proposed framework is validated through cycle‐aging tests and compared against conventional DC, open‐circuit voltage, and EIS‐based methods. Results demonstrate that the fusion model achieves significantly higher accuracy across varying conditions, particularly under both room‐ and high‐temperature environments. These findings highlight the effectiveness of combining electrical and electrochemical models for reliable onboard SOH estimation, providing a pathway for more accurate and practical battery management strategies in EVs.
Kim et al. (Sun,) studied this question.
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