Version 1
: Received: 10 April 2023 / Approved: 11 April 2023 / Online: 11 April 2023 (03:30:10 CEST)
How to cite:
Xing, L.; Luo, W.; Liu, X.; Xiang, B. SOC Estimation for Lithium Batteries Based on Fractional Order Model and Robust Unscented Kalman Filter. Preprints2023, 2023040181. https://doi.org/10.20944/preprints202304.0181.v1
Xing, L.; Luo, W.; Liu, X.; Xiang, B. SOC Estimation for Lithium Batteries Based on Fractional Order Model and Robust Unscented Kalman Filter. Preprints 2023, 2023040181. https://doi.org/10.20944/preprints202304.0181.v1
Xing, L.; Luo, W.; Liu, X.; Xiang, B. SOC Estimation for Lithium Batteries Based on Fractional Order Model and Robust Unscented Kalman Filter. Preprints2023, 2023040181. https://doi.org/10.20944/preprints202304.0181.v1
APA Style
Xing, L., Luo, W., Liu, X., & Xiang, B. (2023). SOC Estimation for Lithium Batteries Based on Fractional Order Model and Robust Unscented Kalman Filter. Preprints. https://doi.org/10.20944/preprints202304.0181.v1
Chicago/Turabian Style
Xing, L., Xiao Liu and Biao Xiang. 2023 "SOC Estimation for Lithium Batteries Based on Fractional Order Model and Robust Unscented Kalman Filter" Preprints. https://doi.org/10.20944/preprints202304.0181.v1
Abstract
Lithium batteries are widely used due to their advantages such as high energy density, stable performance, low pollution, and long recyclable life. Accurate SOC estimation is important for the use of lithium batteries. To address the problem of low accuracy of SOC estimation by traditional methods, this paper proposes a joint method of fractional order robust unscented Kalman filter (FORUKF) and robust unscented Kalman filter (RUKF) to estimate SOC. The method is based on a fractional order model (FOM) that combines the unscented transformation (UT) technique, the H∞ observer and the joint estimator. Specifically, an adaptive genetic algorithm (AGA) was first used to identify the parameters of the FOM for lithium batteries and to verify the accuracy of the model. Estimation and updating of the ohmic resistance R0 and the capacity QN in the model in real-time by RUKF and then estimation of the SOC by FORUKF. Finally, the accuracy of FORUKF-RUKF was verified under the Federal Urban Driving Schedule (FUDS), US06 Highway Driving Schedule and Beijing Dynamic Stress Test (BJDST). According to the results, the FORUKF-RUKF estimated SOC was found to have better accuracy and robustness than the RUKF, FOUKF and EKF.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
