Real-time estimation of state-of-charge in lithium-ion batteries using improved central difference transform method

Abstract

Abstract The accurate and real-time estimation of state-of-charge (SOC) in lithium-ion batteries (LIBs) are crucial for battery management system (BMS) in electric vehicles. The SOC estimation is affected by several factors like temperature, aging and many other battery characteristics, making it challenging. In this study, improved central difference transform Kalman filter method based on square root second-order central difference transform (SRCDKF) was utilized for real-time estimation of SOC in LIBs. The hybrid pulse power characterization (HPPC) tests were combined with recursive least squares (RLS) method to identify the second-order equivalent circuit model parameters. To avoid high order Taylor series expansion and complicated multi-parameter adjustment in other Kalman filters, the CDKF with square root second-order difference transform is developed to generate Sigma point. The effectiveness of the proposed SRCDKF was then verified by pulse discharge and urban dynamometer driving schedule (UDDS) testing, and the results were compared with those obtained from extended Kalman filter (EKF) and unscented Kalman filter (UKF). In particular, the proposed algorithm provided small error within 2% under UDDS testing, and the convergence time was earlier than those obtained with the other two algorithms. The proposed SRCDKF can also guarantee the non-negative covariance and reduce the computational complexity. Overall, these findings look promising for future BMS SOC estimation in practice.