An innovative square root - untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries.

Wang, Shunli; Dang, Quan; Gao, Zhengqing; Li, Bowen; Fernandez, Carlos; Blaabjerg, Frede

Abstract

In the context of the thriving development of new energy vehicles, lithium-ion batteries, as a crucial component of the power storage system, will increasingly contribute to the strategic advancement of the industry, while this paper addresses three key issues in the estimation of lithium-ion battery state of charge (SOC) and state of power (SOP). Firstly, an online modified square root - untraced Kalman filtering (SR-UKF) algorithm is proposed to analyze the impact of temperature-induced capacity fluctuations, achieving highly accurate and adaptive SOC tracking. Secondly, an online multi-limit factor fusion analysis SOP estimation method is designed to mitigate computational complexity and enhance algorithm feasibility by addressing parameter fitting issues during offline identification. Thirdly, a real-time tracking data-based full-parameter online identification method is developed to enhance the accuracy of parameter identification and effectively describe internal and external factors. Experimental results demonstrate the algorithm's high accuracy, with a voltage simulation error below 0.04 V. Compared to traditional methods, the SR-UKF algorithm exhibits lower SOC simulation error below 2.36 %, offering a novel approach for SOC estimation under ambient temperature influences. Moreover, the proposed algorithm effectively estimates SOP, with a peak power estimation error of down to 66 W. In conclusion. This paper presents a novel SOC and SOP evaluation strategy, achieving a more reliable and accurate estimate under varying operating conditions.

Citation

WANG, S., DANG, Q., GAO, Z., LI, B., FERNANDEZ, C. and BLAABJERG, F. 2024. An innovative square root - untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries. Journal of energy storage [online], 104(part B), article number 114555. Available from: https://doi.org/10.1016/j.est.2024.114555

• Journal Article Type: Article
• Acceptance Date: Nov 7, 2024
• Online Publication Date: Nov 16, 2024
• Publication Date: Dec 20, 2024
• Deposit Date: Nov 21, 2024
• Publicly Available Date: Nov 17, 2025
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 104
• Issue: part B
• Article Number: 114555
• DOI: https://doi.org/10.1016/j.est.2024.114555
• Keywords: Lithium-ion battery; State of charge; State of power; Full-parameter online identification; Online square root untraced Kalman; Multi-constraint factor fusion analysis
• Public URL: https://rgu-repository.worktribe.com/output/2584614