A computationally efficient adaptive online state-of-charge observer for Lithium-ion battery for electric vehicle

Abstract

Due to the large number of cells installed in electric vehicle (EV), its battery management system (BMS) requires efficient state of charge (SOC) estimation algorithm. Since there is an impetus to reduce the computational burden (while retaining an acceptable accuracy), this paper proposes a simple and fast online adaptive observer for SOC estimation of Lithium-ion battery. The observer has several attractive features: first, its stability is proven by Lyapunov approach where asymptotic error convergence is guaranteed. Second, the computational requirements are low since it contains a few simple recursive equations without matrix inversion. Third, it is adaptive and achieves simultaneous online estimation of SOC and most of the battery parameters. The practical implementation using a 3 Ah battery proves the effectiveness of the proposed observer under dynamic stress test (DST). The testing with real EV profiles (supplemental federal test procedure which is known as US06 and federal urban driving schedule FUDS) is also performed to show the reliability. It is confirmed that the computation time of the proposed algorithm is reduced by approximately 2.5 times in compared to the extended Kalman filter-recursive least square (EKF-RLS) method. Despite the reduction in computation time, the errors are comparable to the latter. The low computational cost is significant when considering the need to accurately estimate the SOC of a large number of cells in a battery pack of an EV.