Lithium-ion Battery State of Charge/State of Health Estimation Using SMO for EVs

Advanced battery management systems (BMS) in electric vehicles (EVs) require immediate and accurate battery state, such as State-of-Charge (SoC) and State-of-Health (SoH) for efficient monitoring and control. To improve the state estimation performance of battery, an electrochemical model is applied in this paper. First, the electrochemical model is reduced to describe the instantaneous Li-ion concentration dynamics of each electrode sufficiently without main information loss. Second, two separate sliding mode observers (SMOs) combined with reduced order electrochemical model are designed to identify SoC/SoH of lithium-ion cell from external measured voltage and current value. An estimation scheme which is comprised of two subestimators is designed. They work jointly: one separate sliding mode observer (SMO) for SoC estimation using Li-ion solid-electrolyte concentration and the other observer for cell contact resistance adopting Lyapunov's stability theory. Finally, in order to demonstrate the performance of proposed scheme, the simulations are verified by experiments from a 2.3Ah high-power LiFePO₄/graphite cell used in EVs. The results indicate that the proposed estimation scheme with the SMO algorithm performs well with initial error values. The maximum SoC and SoH estimation error are less than 3% and 2.5% under Urban Dynamometer Driving Schedule (UDDS) drive cycles.