State-of-charge estimation of valve regulated lead acid battery based on multi-state Unscented Kalman Filter

As a vital part of the battery-employed system, battery management system (BMS) must correctly estimate values descriptive of the battery’s present operating condition. As is known to all, state-of-charge (SOC) is a key battery state for BMS to estimate. In this paper, based on Unscented Kalman Filter (UKF) theory and a comprehensive battery model, a novel SOC estimation method is proposed. A nonlinear mapping process is involved to recursively calculate the system state variable, thus the errors caused by Extended Kalman Filter (EKF) can be effectively restrained; besides, compared with many simple battery models recently, the comprehensive model presented in this paper can track the operating performance of valve regulated lead acid (VRLA) battery more correctly. The whole estimation process is clearly given; then EKF and UKF are compared through experimental analysis; the results show that UKF method is superior to EKF method in SOC estimation for VRLA battery.