An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect.

Wang, Shun-Li; Fernandez, Carlos; Cao, Wen; Zou, Chuan-Yun; Yu, Chun-Mei; Li, Xiao-Xia

Abstract

The battery modeling and iterative state calculation in the battery management system is very important for the high-power lithium-ion battery packs, the accuracy of which affects its working performance and safety. An adaptive improved unscented Kalman filtering algorithm is developed to realize the iterative calculation process, aiming to overcome the rounding error in the numerical calculation treatment when it is used to estimate the nonlinear state value of the battery pack. As the sigma point is sampled in the unscented transform round from the unscented Kalman filter algorithm, an imaginary number appears that results in the working state estimation failure. In order to solve this problem, the decomposition is combined with the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The estimation error remains 1.60% under the drastic voltage and current change conditions, which can reduce the estimation error by 1.00% compared with the traditional method. It can provide a theoretical safety protection basis of the energy management for the lithium-ion battery pack.

Citation

WANG, S.-L., FERNANDEZ, C., CAO, W., ZOU, C.-Y., YU, C.-M. and LI, X.-X. 2019. An adaptive working state iterative calculation method of the power battery by using the improved Kalman filtering algorithm and considering the relaxation effect. Journal of power sources [online], 428, pages 67-75. Available from: https://doi.org/10.1016/j.jpowsour.2019.04.089

• Journal Article Type: Article
• Acceptance Date: Apr 21, 2019
• Online Publication Date: Apr 30, 2019
• Publication Date: Jul 15, 2019
• Deposit Date: May 27, 2019
• Publicly Available Date: May 1, 2020
• Journal: Journal of power sources
• Print ISSN: 0378-7753
• Electronic ISSN: 1873-2755
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 428
• Pages: 67-75
• DOI: https://doi.org/10.1016/j.jpowsour.2019.04.089
• Keywords: Power battery; Relaxation effect; Iterative state calculation; Unscented Kalman filter; Adaptive covariance matching
• Public URL: https://rgu-repository.worktribe.com/output/244335
• Contract Date: May 27, 2019