Joint Estimation of SOC of Lithium Battery Based on Dual Kalman Filter

In order to improve the estimation accuracy of state charge (SOC) electric vehicle power batteries, a dual Kalman filter method based on online identification model parameters is proposed estimate in lithium-ion batteries. Here, we build first-order equivalent circuit batteries and derive its extended (EKF). Considering that noise value EKF algorithm difficult select through experiments achieve best filtering effect, this paper combines an improved particle swarm optimization (IPSO) with realize parameter identification. At same time, derived from space equation also used SOC estimation. It constitutes for The experimental simulation results show IPSO–EKF can adaptively adjust according complex operating conditions vehicles. Compared algorithm, our identify battery more accurately. composed applied achieved higher final verification.