An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries.

Wang, Shunli; Wang, Chao; Takyi-Aninakwa, Paul; Jin, Siyu; Fernandez, Carlos; Huang, Qi

Abstract

The State estimation and determination of time-varying model parameters are crucial for ensuring the safe management of lithium-ion batteries. This paper designs a limited memory recursive least square algorithm to improve the accuracy of online parameter identification. An adaptive radial basis correction-differential support vector machine model is constructed to correct the state of charge value by considering the dynamic characteristic parameters. It greatly reduces estimation error and noise, while monitoring the critical conditions for safe and reliable online battery operation. The estimation effects of the proposed model are verified under hybrid pulse power characterization and dynamic stress test working conditions. The maximum error values obtained are 0.037% and 0.336%, respectively, thus achieving high-precision estimation. The proposed method is adaptive to real-time battery management applications, laying a foundation for robust state estimation of lithium-ion batteries used in urban transportation electric vehicles.

Citation

WANG, S., WANG, C., TAKYI-ANINAKWA, P., JIN, S., FERNANDEZ, C. and HUANG, Q. 2024. An improved parameter identification and radial basis correction-differential support vector machine strategies for state-of-charge estimation of urban-transportation-electric-vehicle lithium-ion batteries. Journal of energy storage [online], 80, article number 110222. Available from: https://doi.org/10.1016/j.est.2023.110222

• Journal Article Type: Article
• Acceptance Date: Dec 19, 2023
• Online Publication Date: Dec 28, 2023
• Publication Date: Mar 1, 2024
• Deposit Date: Jan 11, 2024
• Publicly Available Date: Dec 29, 2024
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 80
• Article Number: 110222
• DOI: https://doi.org/10.1016/j.est.2023.110222
• Keywords: Lithium-ion battery; Limited memory recursive least square; Adaptive radial basis correction; Differential support vector machine; Adaptive extended Kalman filter
• Public URL: https://rgu-repository.worktribe.com/output/2204394