Co-estimation of state-of-charge and state-of-health for high-capacity lithium-ion batteries.

Xiong, Ran; Wang, Shunli; Feng, Fei; Yu, Chunmei; Fan, Yongcun; Cao, Wen; Fernandez, Carlos

Abstract

To address the challenges of efficient state monitoring of lithium-ion batteries in electric vehicles, a co-estimation algorithm of state-of-charge (SOC) and state-of-health (SOH) is developed. The algorithm integrates techniques of adaptive recursive least squares and dual adaptive extended Kalman filtering to enhance robustness, mitigate data saturation, and reduce the impact of colored noise. At 25 °C, the algorithm is tested and verified under dynamic stress test (DST) and Beijing bus DST conditions. Under the Beijing bus DST condition, the algorithm achieves a mean absolute error (MAE) of 0.17% and a root mean square error (RMSE) of 0.19% for SOC estimation, with a convergence time of 4 s. Under the DST condition, the corresponding values are 0.05% for MAE, 0.07% for RMSE, and 5 s for convergence time. Moreover, in this research, the SOH is described as having internal resistance. Under the Beijing bus DST condition, the MAE and the RMSE of the estimated internal resistance of the proposed approach are 0.018% and 0.075%, with the corresponding values of 0.014% and 0.043% under the DST condition. The results of the experiments provide empirical evidence for the challenges associated with the efficacious estimation of SOC and SOH.

Citation

XIONG, R., WANG, S., FENG, F., YU, C., FAN, Y., CAO, W. and FERNANDEZ, C. 2023. Co-estimation of state-of-charge and state-of-health for high-capacity lithium-ion batteries. Batteries [online], 9(10), article number 509. Available from: https://doi.org/10.3390/batteries9100509

• Journal Article Type: Article
• Acceptance Date: Oct 10, 2023
• Online Publication Date: Oct 12, 2023
• Publication Date: Oct 31, 2023
• Deposit Date: Oct 12, 2023
• Publicly Available Date: Oct 12, 2023
• Journal: Batteries
• Electronic ISSN: 2313-0105
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 10
• Article Number: 509
• DOI: https://doi.org/10.3390/batteries9100509
• Keywords: State-of-charge; State-of-health; Adaptive recursive least squares; Dual adaptive extended; Kalman filtering
• Public URL: https://rgu-repository.worktribe.com/output/2107625