An optimal nonlinear observer for state-of-charge estimation of lithium-ion batteries

As the soaring development of electric vehicles and distributed generation systems, lithium-ion battery has been commonly used for energy storage. Accurate estimation of state of charge (SOC) is crucial for charging or discharging the batteries safely and reliably. However, the SOC is immeasurable and nonlinearly varies with factors (e.g., current rate, battery degeneration, ambient temperature and measurement noise), a reliable and robust algorithm for SOC estimation is accordingly expected. In this paper, an optimal nonlinear observer (ONLO) for SOC estimation is proposed. The particle swarm optimization algorithm is employed to optimize parameters of the nonlinear observer. The proposed approach is verified by experiments performed on INR18650-25R lithium-ion batteries produced by SAMSUMG SDI. Experimental results indicate that the proposed ONLO can accurately estimate the battery SOC with a mean absolute error of 1.8% and a maximum error of less than 6.5%, which are both lower than that of the unscented Kalman filter (UKF). Furthermore, the computation cost of the ONLO is reduced to 30% compared with the UKF.