Comparison of Kalman Filter-based State of Charge Estimation Strategies for Li-Ion Batteries

Currently, the automotive industry is experiencing a significant technology shift from internal combustion engine propelled vehicles to second generation battery electric vehicles (BEVs), hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). The battery pack represents the core of the electric vehicle powertrain and its most expensive component and therefore requires continuous condition monitoring and control. As such, extensive research has been conducted to estimate the battery critical parameters such as state-of-charge (SOC) and state-of-health (SOH). In order to accurately estimate these parameters, a high fidelity battery model has to work collaboratively with a robust estimation strategy onboard of the battery management system (BMS). In this paper, three Kalman Filter-based estimation strategies are analyzed and compared, namely: The Extended Kalman Filter (EKF), Sigma-point Kalman filtering (SPKF) and Cubature Kalman filter (CKF). These estimation strategies have been compared based on the first-order equivalent circuit-based model. Estimation strategies have been compared based on their SOC estimation accuracy, robustness to initial SOC error and computation requirement.