Multi-physics coupling model parameter identification of lithium-ion battery based on data driven method and genetic algorithm

Wencan Zhang, Yi Xie, Hancheng He, Zhuoru Long, Liyang Zhuang and Jianjie Zhou

Energy, 2025, vol. 314, issue C

Abstract: The increasing demand for electric vehicles necessitates accurate battery modeling to ensure performance, safety, and longevity. This study develops a comprehensive coupled mechanism model for lithium-ion batteries that integrates electrochemical, aging, and thermal phenomena. To address the challenge of identifying numerous unknown parameters within the model, a data-driven approach is employed. First, Latin Hypercube Sampling is employed to generate a diverse set of parameter combinations. Subsequently, the coupled mechanism model is simulated using these combinations to produce a dataset of macroscopic responses. This dataset is utilized to train an artificial neural network, creating a meta-model that significantly accelerates the optimization process. Following this, sensitivity analysis is conducted to identify the most influential parameters. Finally, a genetic algorithm is used to optimize these parameters, minimizing the discrepancy between model predictions and experimental data. Results reveal that among 33 model parameters, 9 high-sensitivity parameters and 10 medium-sensitivity parameters are identified as significantly influencing the model output. By refining these parameters, the model achieved mean absolute errors of 0.0147, 0.2132, and 0.0163 for voltage, temperature, and capacity simulations, respectively. These results demonstrate the high accuracy and effectiveness of the proposed approach, offering a robust and efficient method for lithium-ion battery modeling.

Keywords: Lithium-ion batteries; Meta-model; Artificial neural network; Parameter identification; Genetic algorithm (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224038982
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:314:y:2025:i:c:s0360544224038982

DOI: 10.1016/j.energy.2024.134120

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().