An improved random drift particle swarm optimization-feed forward backpropagation neural network for high-precision state-of-charge estimation of lithium-ion batteries.

Hai, Nan; Wang, Shunli; Liu, Donglei; Gao, Haiying; Fernandez, Carlos

Abstract

A predictive model with high accuracy and stability of the state of charge (SOC) estimation for lithium-ion batteries plays a significant role in electric vehicles. An improved random drift particle swarm optimization-feed forward backpropagation neural network (IRDPSO-FFBPNN) is established in this paper. Basically, a three-layer FFBPNN is established, and its learning process is analyzed in detail. Then, to avoid the particle out-of-control, inducting weight parameter σ to achieve dynamic control weight convergence. What's more, the cross-reorganization of data is proposed to enhance the utilization. Finally, a further performance comparison with other networks is made under different working conditions to prove the effectiveness of the IRDPSO-FFBPNN. The experimental results showed that the maximum SOC error of the IRDPSO-FFBPNN is 0.1021% in 45s, 0.1237% in 116s under BBDST and DST with different temperatures, respectively, which performed better both in terms of time-consumption and accuracy.

Citation

HAI, N., WANG, S., LIU, D., GAO, H. and FERNANDEZ, C. 2023. An improved random drift particle swarm optimization-feed forward backpropagation neural network for high-precision state-of-charge estimation of lithium-ion batteries. Journal of energy storage [online], 73(part D), article number 109286. Available from: https://doi.org/10.1016/j.est.2023.109286

• Journal Article Type: Article
• Acceptance Date: Oct 10, 2023
• Online Publication Date: Oct 16, 2023
• Publication Date: Dec 20, 2023
• Deposit Date: Oct 20, 2023
• Publicly Available Date: Oct 17, 2024
• Journal: Journal of energy storage
• Print ISSN: 2352-152X
• Electronic ISSN: 2352-1538
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 73
• Issue: Part D
• Article Number: 109286
• DOI: https://doi.org/10.1016/j.est.2023.109286
• Keywords: Backpropagation; Random particle swarm; Dynamic weight adjustment; Cross-reorganization; State of charge; Lithium-ion batteries
• Public URL: https://rgu-repository.worktribe.com/output/2114416