Modeling based SOC estimation for commercial EV Li-ion batteries under vibration conditions

Abstract

Modeling-based adaptive estimation techniques, such as the extended Kalman filter (EKF), have been widely used for state of charge (SOC) estimation in Li-ion batteries due to their robustness and noise rejection capability. However, conventional EKF-based SOC estimation methods primarily reply on equivalent circuit models (ECMs), which often neglect the effects of mechanical loading and vibration-induced capacity degradation in real-world applications. In this study, a novel SOC estimation framework is developed by integrating vibration-induced capacity degradation into an electrochemical single-particle model (SPM) coupled with an EKF. In this SPM-EKF approach, the effective capacity is treated as a dynamic state influenced by vibration intensity, which improves the physical accuracy of SOC estimation under mechanical loading conditions. The SPM captures the nonlinear electrochemical behavior of the battery, while the EKF enables real-time estimation of SOC under capacity degradation. This developed framework enables more accurate SOC estimation for onboard batteries operating in vibration environments such as electrical vehicles. The obtained simulation and experimental validation results demonstrate that the proposed SPM-EKF method achieves high SOC estimation accuracy for different chemistry commercial cells when vibration-induced capacity variation is present.