An improved robust function correction-adaptive extended Kalman filtering algorithm for SOC estimation of lithium-ion batteries.

Zhu, Chenyu; Wang, Shunli; Yu, Chunmei; Hai, Nan; Fernandez, Carlos; Sun, Zhenhua

Abstract

State of Charge (SOC) is one of the key indicators for evaluating the state of electric vehicles. In order to cope with the uncertainty of random noise in nonlinear systems, an improved robust function correction-adaptive extended Kalman filtering (RFC-AEKF) algorithm is proposed for SOC prediction. Using FFRLS method to verify the Dual Polarization model established in this paper. The robust function is an abstract method that describes system state noise and observation noise, and performs real-time correction, combined with adaptive methods to estimate SOC. The experimental results show that the proposed RFC-AEKF algorithm has the smallest mean absolute error (MAE) and root mean square error (RMSE) compared to other algorithms. Under the Beijing bus dynamic stress test (BJDST) conditions, the MAE and RMSE of the RFC-AEKF are 0.354% and 0.658%, respectively, indicating that the RFC-AEKF algorithm can improve SOC estimation accuracy and enhance robustness.

Citation

ZHU, C., WANG, S., YU, C., HAI, N., FERNANDEZ, C. and SUN, Z. 2023. An improved robust function correction-adaptive extended Kalman filtering algorithm for SOC estimation of lithium-ion batteries. In Proceedings of the 3rd New energy and energy storage system control summit forum 2023 (NEESSC 2023), 26-28 September 2023, Mianyang, China. Piscataway: IEEE [online], pages 358-362. Available from: https://doi.org/10.1109/NEESSC59976.2023.10349263

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 3rd New energy and energy storage system control summit forum 2023 (NEESSC 2023)
• Start Date: Sep 26, 2023
• End Date: Sep 28, 2023
• Acceptance Date: Sep 20, 2023
• Online Publication Date: Dec 31, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Feb 1, 2024
• Publicly Available Date: Feb 2, 2024
• Peer Reviewed: Peer Reviewed
• Pages: 358-362
• DOI: https://doi.org/10.1109/NEESSC59976.2023.10349263
• Keywords: Lithium-ion battery; SOC; Robust function correction; Adaptive extended Kalman filtering
• Public URL: https://rgu-repository.worktribe.com/output/2225977