A novel combined estimation method for state of energy and predicted maximum available energy based on fractional-order modeling.

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

Abstract

Although accurate SOE estimation can enhance the reliability of residual energy prediction, the environmental temperature, parameter coupling, and multiple state constraints increase the difficulty of obtaining SOE accurately. A combined estimation method for SOE and predicted maximum available energy based on fractional-order composite equivalent circuit model is proposed to ensure SOE accuracy in the whole battery life cycle. Firstly, the fixed fractional-order forgetting factor recursive least square method is used to realize the online identification of full parameters. Secondly, the adaptive dual fractional-order extended Kalman filter algorithm is applied to realize the co-estimation of SOC and SOE to solve parameter constraints and state coupling. Finally, the fourth-order extended Kalman filter algorithm is exploited to realize the joint estimation of the predicted maximum available energy and SOE, effectively avoiding the divergence of results caused by fixed maximum available energy. The longitudinal comparison experiment results show that the proposed algorithm has the highest accuracy and the smallest root mean square error, which proves the necessity of updating the maximum available energy in real-time. The horizontal comparison experiment further illustrates that real-time correction of multiple factors affecting the SOE estimation accuracy is a necessary way to achieve high accuracy and strong robustness.

Citation

CHEN, L., WANG, S., JIANG, H. and FERNANDEZ, C. 2023. A novel combined estimation method for state of energy and predicted maximum available energy based on fractional-order modeling. Journal of energy storage [online], 62, article 106930. Available from: https://doi.org/10.1016/j.est.2023.106930

• Journal Article Type: Article
• Acceptance Date: Feb 18, 2023
• Online Publication Date: Mar 1, 2023
• Publication Date: Jun 30, 2023
• Deposit Date: Mar 16, 2023
• Publicly Available Date: Mar 2, 2024
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 62
• Article Number: 106930
• DOI: https://doi.org/10.1016/j.est.2023.106930
• Keywords: Li-ion battery; Fractional-order composite equivalent circuit model; Maximum available energy prediction; State of energy; Adaptive double fractional-order extended Kalman filter
• Public URL: https://rgu-repository.worktribe.com/output/1912503