Sheng Li
An improved pelican optimization-kernel extreme learning machine for highly accurate state of charge estimation of lithium-ion batteries in energy storage systems.
Li, Sheng; Wang, Shunli; Cao, Wen; Zhang, Liya; Fernandez, Carlos
Authors
Abstract
The accurate estimation of the state of charge (SOC) of lithium-ion batteries is crucial for real-time monitoring and safety control. This paper proposes a novel method for estimating SOC by optimizing the kernel extreme learning machine (KELM) with a radial basis function (RBF) kernel using an enhanced pelican optimization algorithm (POA), termed TWCS-PO-KELM. This approach addresses the challenges of real-time estimation and low accuracy in conventional methods. This paper improves the basic POA by incorporating Tent chaotic mapping to diversify the initial population, a nonlinear inertia weight factor to improve local optimization, and a Cauchy variation alongside a sparrow alert mechanism to enhance the algorithm's robustness and optimization performance. The KELM model, based on the RBF kernel, enables efficient non-linear mapping of the input features, improving the accuracy of SOC estimation. Experimental results demonstrate that the TWCS-PO-KELM model offers superior SOC estimation with a mean absolute error (MAE) of 0.143%, root mean square error (RMSE) of 0.172%, and mean absolute percentage error (MAPE) of 1.344% under BBDST conditions, showcasing its strong tracking ability and robustness in comparison to other methods.
Citation
LI, S., WANG, S., CAO, W., ZHANG, L. and FERNANDEZ, C. [2025]. An improved pelican optimization-kernel extreme learning machine for highly accurate state of charge estimation of lithium-ion batteries in energy storage systems. Ionics [online], Online First. Available from: https://doi.org/10.1007/s11581-025-06327-9
| Journal Article Type | Article |
|---|---|
| Acceptance Date | Apr 16, 2025 |
| Online Publication Date | Apr 28, 2025 |
| Deposit Date | May 16, 2025 |
| Publicly Available Date | Apr 29, 2026 |
| Journal | Ionics |
| Print ISSN | 0947-7047 |
| Electronic ISSN | 1862-0760 |
| Publisher | Springer |
| Peer Reviewed | Peer Reviewed |
| DOI | https://doi.org/10.1007/s11581-025-06327-9 |
| Keywords | Kernel extreme learning machine; Lithium-ion battery; Pelican optimization algorithm; State of charge; TWCS-PO-KELM model |
| Public URL | https://rgu-repository.worktribe.com/output/2830445 |
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