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A hybrid algorithm based on beluga whale optimization-forgetting factor recursive least square and improved particle filter for the state of charge estimation of lithium-ion batteries.

Shen, Xianfeng; Wang, Shunli; Yu, Chunmei; Qi, Chuangshi; Li, Zehao; Fernandez, Carlos

Authors

Xianfeng Shen

Shunli Wang

Chunmei Yu

Chuangshi Qi

Zehao Li



Abstract

Battery state of charge (SOC) is crucial in power battery management systems for improving the efficiency of battery use and its safety performance. In this paper, we propose a forgotten factor recursive least squares (FFRLS) method based on the beluga whale optimization (BWO) and an improved particle filtering (PF) algorithm for estimating the SOC of lithium batteries with ternary lithium batteries as the research object. Firstly, to address the accuracy deficiencies of the FFRLS method, the optimal parameter initial value and the forgetting factor value are optimized by using the BWO algorithm. Secondly, the adaptive simulated annealing algorithm (ASA) is introduced into the particle swarm optimization (PSO) to solve the sub-poor problem of traditional particle filtering. Experimental validation is performed by constructing complex working conditions, and the results show that the maximum error of parameter identification using the BWO-FFFRLS algorithm is stable within 2%. The MAE and RMSE are limited to within 2% when the ASAPSO-PF algorithm is applied to estimate the SOC estimation under Beijing Bus Dynamic Stress Test (BBDST), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization Test (HPPC) working conditions, indicating that the proposed algorithm has strong tracking capability and robustness for lithium battery SOC.

Citation

SHEN, X., WANG, S., YU, C., QI, C., LI, Z. and FERNANDEZ, C. 2023. A hybrid algorithm based on beluga whale optimization-forgetting factor recursive least square and improved particle filter for the state of charge estimation of lithium-ion batteries. Ionics [online], 29(10), pages 4351-4363. Available from: https://doi.org/10.1007/s11581-023-05147-z

Journal Article Type Article
Acceptance Date Jul 25, 2023
Online Publication Date Aug 14, 2023
Publication Date Oct 31, 2023
Deposit Date Sep 5, 2023
Publicly Available Date Aug 15, 2024
Journal Ionics
Print ISSN 0947-7047
Electronic ISSN 1862-0760
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 29
Issue 10
Pages 4351-4363
DOI https://doi.org/10.1007/s11581-023-05147-z
Keywords Lithium-ion battery; Second-order RC-PNGV model; BWO-FFRLS algorithm; ASAPSO-PF algorithm; State of charge
Public URL https://rgu-repository.worktribe.com/output/2049146