A novel fractional-order extended Kalman filtering method for on-line joint state estimation and parameter identification of the high power li-ion batteries.

Chen, Lei; Wang, Shunli; Jiang, Hong; Fernandez, Carlos; Xiong, Xin

Abstract

To ensure the reliability and sustainability of the energy storage system, it is important to accurately estimate the state of charge of the battery management system. The Li-ion battery is established based on fractional-order model, and the model parameters are identified online using particle swarm optimization combined with the forgetting factor recursive least square method. On this basis, a novel fractional-order extended Kalman filter method for on-line joint state estimation and parameter identification is proposed. This method can update the parameter model of Li-ion battery in real-time, which not only improves the accuracy of the battery model but also improves the accuracy of SOC estimation. Finally, to verify the accuracy and superiority of the method, the integral order extended Kalman filter, fractional-order extended Kalman filter are compared with the proposed method under the BBDST test schedule. Experimental results show that the algorithm has the highest SOC estimation accuracy and the smallest estimation error (1.5 %.). The results indicate that the fractional-order model can better describe the dynamic characteristics of Li-ion battery, and the adaptive scheme can significantly suppress noise measurement errors and battery model errors. The algorithm realizes online parameter identification and can be used in engineering applications.

Citation

CHEN, L., WANG, S., JIANG, H., FERNANDEZ, C. and XIONG, X. 2021. A novel fractional-order extended Kalman filtering method for on-line joint state estimation and parameter identification of the high power li-ion batteries. International journal of electrochemical science [online], 16(5), article 210537. Available from: https://doi.org/10.20964/2021.05.64

• Journal Article Type: Article
• Acceptance Date: Mar 16, 2021
• Online Publication Date: Mar 31, 2021
• Publication Date: May 31, 2021
• Deposit Date: May 13, 2021
• Publicly Available Date: May 13, 2021
• Journal: International journal of electrochemical science
• Electronic ISSN: 1452-3981
• Publisher: Electrochemical Science Group
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 5
• Article Number: 210537
• DOI: https://doi.org/10.20964/2021.05.64
• Keywords: Li-ion battery; Fractional-order equivalent circuit model; State of charge; Forgetting factor recursive least-square; Adaptive fractional-order extended Kalman filter
• Public URL: https://rgu-repository.worktribe.com/output/1335377