Comparison of Kalman filter and H-infinity filter for Battery State of Charge Estimation with a Detailed Validation Method

Accurate and reliable estimation of the state of charge (SOC) of lithium-ion batteries is essential for the performance and safety of battery management systems (BMS) in applications such as electric vehicles and energy storage systems. However, there is a lack of comprehensive comparative studies evaluating different SOC estimation methods under standardized conditions. In this paper, we address this gap by providing a comprehensive, objective comparison of various Kalman and H-Infinity filter variants for battery SOC estimation, utilizing a detailed validation method based on well-defined criteria. The main contributions of this work are: (1) Implementation of multiple filter variants using a consistent equivalent circuit battery model; (2) Development of a standardized validation method for objective performance evaluation; (3) Detailed mathematical formulations enhancing reproducibility; (4) Evaluation of computational efficiency on a digital signal processor (DSP) to provide practical insights for real-time applications. Our findings reveal that while neither filter type is universally superior, the Extended Kalman Filter (EKF) and H-Infinity Filter (HIF) offer a solid balance between estimation accuracy and computational load, making them reliable choices for general applications. This work advances the understanding of SOC estimation methods and aids practitioners in balancing accuracy and computational efficiency for real-world applications.