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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

Authors

Lei Chen

Shunli Wang

Hong Jiang

Xin Xiong



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 Open Access
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