Estimation of Model Parameters and SOC of Lithium Batteries Based on IPSO-EKF

doi:10.3969/j.issn.1000-1093.2014.10.021

Abstract

Abstract: An extended Kalman filter (EKF) which is optimized by the improved particle swarm optimization (IPSO) algorithm is proposed to estimate the state-of-charge (SOC) of battery. A new state space equation applied to EKF algorithm is constituted to reduce the influence of non-linear characteristics of parameters, and the optimal estimation of SOC is obtained based on the real-time identification of battery model parameters. IPSO algorithm is applied to optimize the system state error covariance matrix and measurement noise covariance matrix to improve the estimation accuracy of SOC by solving the problems in achieving the optimal solutions of these covariance matrixes. The results show that the IPSO-EKF algorithm can estimate the model parameters and SOC of battery accurately, and correct the state variable initial error.

Key words: electrical engineering, lithium battery, state of charge, model parameter, particle swarm optimization algorithm, extended Kalman filter

CLC Number:

TM911

XIANG Yu， MA Xiao-jun， LIU Chun-guang， KE Rong-shuo， ZHAO Zi-xu. Estimation of Model Parameters and SOC of Lithium Batteries Based on IPSO-EKF[J]. Acta Armamentarii, 2014, 35(10): 1659-1666.

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