A novel combined estimation method of online full-parameter identification and adaptive unscented particle filter for Li-ion batteries SOC based on fractional-order modeling.

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

Abstract

Accurate estimation of the state of charge (SOC) of Li-ion battery can ensure the reliability of the storage system. A combined estimator of online full-parameter identification and adaptive unscented particle filter for Li-ion battery SOC based on an improved fractional-order model is proposed, which overcomes the shortcomings of the traditional SOC cumulative error and the difficulty of OCV acquisition. The proposed adaptive fractional unscented particle filter algorithm introduces fractional parameters as hidden parameters and reduces the complexity of the algorithm iteration by reducing the number of particles. At the same time, the noise adaptive algorithm based on the residual sequence can solve the divergence problem of the filter and improve the adaptability of the algorithm. To verify the feasibility of the algorithm under complex operating conditions, the urban dynamometer driving schedule dynamic working conditions of Li-ion batteries are verified. The experimental results show that the evaluation index of the algorithm is the best, the RMSE is 0.67%, and the SOC estimation is more accurate. It shows that the algorithm has strong robustness and fast convergence.

Citation

CHEN, L., WANG, S., JIANG, H., FERNANDEZ, C. and XIONG, X. 2021. A novel combined estimation method of online full-parameter identification and adaptive unscented particle filter for Li-ion batteries SOC based on fractional-order modeling. International journal of energy research [online], 45(10), pages 15481-15494. Available from: https://doi.org/10.1002/er.6817

• Journal Article Type: Article
• Acceptance Date: Apr 19, 2021
• Online Publication Date: May 5, 2021
• Publication Date: Aug 31, 2021
• Deposit Date: May 14, 2021
• Publicly Available Date: May 6, 2022
• Journal: International Journal of Energy Research
• Print ISSN: 0363-907X
• Electronic ISSN: 1099-114X
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 45
• Issue: 10
• Pages: 15481-15494
• DOI: https://doi.org/10.1002/er.6817
• Keywords: Adaptive fractional-order unscented particle filter; Full-parameter identification; Improve fractional-order equivalent circuit model; Li-ion battery; State of charge
• Public URL: https://rgu-repository.worktribe.com/output/1335468