Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms

Mónica Camas-Náfate, Alberto Coronado-Mendoza, Carlos Vargas-Salgado (), Jesús Águila-León and David Alfonso-Solar
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Mónica Camas-Náfate: Department of Water and Energy Studies, University of Guadalajara, Guadalajara 44430, Mexico
Alberto Coronado-Mendoza: Department of Water and Energy Studies, University of Guadalajara, Guadalajara 44430, Mexico
Carlos Vargas-Salgado: University Institute of Energetic Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Jesús Águila-León: Department of Water and Energy Studies, University of Guadalajara, Guadalajara 44430, Mexico
David Alfonso-Solar: University Institute of Energetic Engineering, Universitat Politècnica de València, 46022 Valencia, Spain

Energies, 2024, vol. 17, issue 4, 1-22

Abstract: In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(n k ), while GWO implied a potential polynomial complexity within the range of O(n k ) or O(2 n ) based on execution times from populations of 10 to 1000.

Keywords: Particle Swarm Optimization (PSO); Grey Wolf Optimizer (GWO); lithium-ion battery modeling; charge-discharge cycle predictions; bio-inspired algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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