Title: Joint estimation of battery state-of-charge based on the genetic algorithm - adaptive unscented Kalman filter
Authors: Hou Zhixiang; Hou Jiqiang
Addresses: School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410004, China ' School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410004, China
Abstract: In order to effectively improve the accuracy of SOC estimation and overcome the problems that the conventional Kalman filter algorithm relies too much on an accurate battery model and the system noise must obey the white Gaussian noise distribution, a joint estimation method of battery state-of-charge based on genetic algorithm - adaptive unscented Kalman filter (GA-AUKF) is proposed in this paper. Firstly, in order to accurately simulate the working mechanism of a battery and express the relationship between the main parameters concerning the battery, online identification of model parameters is performed in this paper through the forgetting factor recursive least-squares (FFRLS) algorithm based on second-order equivalent circuit model. Secondly, in order to weaken the effect of system noise and measurement noise on the accuracy of SOC estimation, the genetic algorithm is adopted to optimise and update the adaptive unscented Kalman filter noise matrix. Finally, FFRLS is combined with GA-AUKF for SOC estimation. Experimental results show that the method proposed in this paper is obviously better than the AUKF algorithm and others in estimation accuracy, and it can effectively reduce the effect of filter noise covariance and improve the estimation accuracy with an estimation error less than 1%.
Keywords: lithium battery; state-of-charge; battery model; unscented Kalman filter; joint estimation.
International Journal of Computing Science and Mathematics, 2021 Vol.14 No.1, pp.1 - 16
Received: 09 Oct 2018
Accepted: 28 Nov 2018
Published online: 12 Oct 2021 *