Estimation of lithium-ion battery state of charge for electric vehicles using a nonlinear state observer

The state of charge (SOC) estimation of lithium-ion batteries is complex due to the various nonlinear uncertainties present in the battery. However, in this paper, a new nonlinear state observer (NSO) is proposed to be designed for the estimation of accurate and robust SOC. This proposed observer is suitable for both continuous and discrete-time nonlinear systems. To design the nonlinear observer, two-RC equivalent circuit model state equations are simulated for the dynamic behavior of the lithium-ion battery. The seventh-order polynomial fitting approach is assumed for the nonlinear relationship between open-circuit voltage (OCV) and SOC, and the exponential fitting method is used to estimate the battery's offline parameters. Lyapunov's stability criterion achieves the stability and convergence capability of the proposed method. An urban dynamometer driving schedule (UDDS) cycle was adopted to estimate the performance of the proposed observer by comparing it with the well-established methods like unscented Kalman filter (UKF) and sliding mode observer (SMO) algorithms, and it was found that the proposed observer achieved better performance like accurate SOC, high convergence capability, and less SOC error.