A Li-ion Battery SOC Estimation Method Based On Improved Particle Swarm Optimization Double Kalman Filter

An real-time estimation method of Li-ion Battery SOC based on the battery equivalent circuit model is proposed in this dissertation,which can achieve the identification of model parameter and Li-ion Battery SOC estimation alternately.Battery discharge tests were implemented and verified that the method can effectively improve the accuracy of Li-ion Battery SOC estimation.Circuit based on second-order RC parallel network is quoted directly in this dissertation and discharge experiments of Li-ion Battery were carried to further analyze and prove the feasibility of the above circuit.In order to research the effect of temperature on the experimental results,experiments are carried out in high and low temperature test chamber.The equivalent circuit model parameters identification is implemented with Improved Particle Swarm Optimization Kalman Filtering(IPSO-KF)due to its reliability and easy implementation.The input of IPSO-KF is current and voltage by sampling with sensor,then the outputs are the parameters of equivalent circuit model.Improved Particle Swarm Optimization is used to optimize the noise covariance matrix in the KF system equation,and its objective function is absolute cumulative error of Predictive and measured values of Li-ion Battery terminal voltage.Inspired by the above algorithm of model parameter identification,Improved Particle Swarm Optimization Double Kalman Filtering(IPSO-DKF)is proposed to resolve the problem of lithium battery SOC real-time estimation.DKF employs two Kalman filter lines running alternately,one is used to model parameter estimation through KF,another is used to estimate the system state through EKF,IPSO is exploited to optimizate the noise covariance matrix of Li-ion Battery system.The effectiveness of the above SOC estimation algorithm is verified by the experiment of lithium battery discharge under complex operating conditions.Compared with the discharge experiment method,the algorithm can effectively correct the initial error and cumulative error when SOC estimation is carried out.