A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions.

Wang, Shun‐Li; Fernandez, Carlos; Xie, Zheng‐Wei ; Li, Xiao‐Xia; Zou, Chuan‐Yun; Li, Qiang

Abstract

A novel real-time state monitoring method is proposed to realize the real-time energy management of the lithium-ion battery packs, which is conducted in the iterative computational calculation process by introducing an improved weighting factor-unscented Kalman filtering algorithm. The accurate state monitoring treatment is investigated by applying a new iterate calculation thought, in which the improved weight coefficient parameter is constructed and its numerical stability is improved. Meanwhile, the recursive calculation is derived by using the real-time measured factors, according to which the state-of-charge estimation is realized accurately. Aiming to adapt the complex current variation working conditions, the nonlinear treatment is introduced to construct the mathematical unscented transforming function. As can be known from the experimental results, the state-of-charge estimation accuracy is 98.34% under the complex current charge-discharge working conditions. Meanwhile, the effective closed-circuit voltage trackage is also investigated accurately and its tracking error is within 3.51% in the complex working conditions, which provides a good security guarantee for the reliable energy supply of the lithium-ion battery packs.

Citation

WANG, S.-L., FERNANDEZ, C. XIE, Z.-W., LI, X.-X., ZOU, C.-Y. and LI, Q. 2019. A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions. Energy science and engineering [online], 7(6), pages 3038-3057. Available from: https://doi.org/10.1002/ese3.478

• Journal Article Type: Article
• Acceptance Date: Aug 28, 2019
• Online Publication Date: Nov 11, 2019
• Publication Date: Dec 31, 2019
• Deposit Date: Nov 21, 2019
• Publicly Available Date: Nov 21, 2019
• Journal: Energy Science and Engineering
• Print ISSN: 2050-0505
• Electronic ISSN: 2050-0505
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 6
• Pages: 3038-3057
• DOI: https://doi.org/10.1002/ese3.478
• Keywords: Complex current variation; Kalman filter; Lithium-ion battery; State monitoring; Unscented transform; Weight coefficient
• Public URL: https://rgu-repository.worktribe.com/output/782988