Optimized State of Charge Estimation of Lithium-Ion Battery in SMES/Battery Hybrid Energy Storage System for Electric Vehicles

Abstract

With the increasing capacity of large-scale electric vehicles, it's necessary to stabilize the fluctuation of charging voltage in order to achieve improvement of lithium-ion battery lifecycle, and the hybrid energy storage system (HESS) including superconducting magnetic energy storage (SMES) and lithium-ion battery is introduced, which is significant to reduce the high frequency discharge cycles and to manage the state of charge (SOC) for batteries with an optimized integration scheme based on power grading approach. With the unscented Kalman filter (UKF) method for battery SOC estimation and the extended Kalman filter (EKF) method for battery internal resistance estimation, the approach for estimating the lithium-ion battery state through adaptive unscented Kalman filter (AUKF) is derived. The AUKF approach takes the form of iterative loop to estimate system parameters and state, so the system strategy has well adaptive characteristics. Experimental results illustrate that the SOC estimation based on the proposed optimized approach for lithium-ion battery in HESS can achieve the well robustness and fast convergence.