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An asynchronous electro-thermal coupling modeling method of lithium-ion batteries under dynamic operating conditions

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  • Liu, Wei
  • Teh, Jiashen
  • Alharbi, Bader

Abstract

Accurate parameter estimation is essential for assessing the performance of lithium-ion batteries (LIBs) in electric vehicles (EVs) and energy storage systems. However, the electro-thermal coupling effects within LIBs under dynamic operating conditions pose significant challenges for battery parameter identification and modeling. To address these challenges, this paper proposes an asynchronous electro-thermal coupling modeling method of LIBs, integrating a third-order RC equivalent circuit model (3RC_ECM) and a thermal model (TM). An innovative OCV identification method based on curve transformation is proposed, and an analytical OCV model based on the Nernst equation is developed. Accounting for real-time variation of OCV, an asynchronous parameter modeling method is proposed to identify different frequency parameters of 3RC_ECM. The key thermal parameters of TM are identified using the particle swarm optimization (PSO) algorithm based on the proposed OCV model. Experimental results validate that the method achieves a root mean square error (RMSE) below 9.38 mV, a mean absolute error (MAE) below 7.02 mV, and a mean absolute percentage error (MAPE) below 2.16 ‰, demonstrating superior accuracy and adaptability compared to traditional ECMs. The proposed model also offers robust parameter data and technical insights for battery state estimation under dynamic operational conditions.

Suggested Citation

  • Liu, Wei & Teh, Jiashen & Alharbi, Bader, 2025. "An asynchronous electro-thermal coupling modeling method of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225015324
    DOI: 10.1016/j.energy.2025.135890
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    2. Liu, Wei & Teh, Jiashen, 2025. "Remaining useful life prediction of lithium-ion batteries based on an incremental internal resistance aging model and a gated recurrent unit neural network," Energy, Elsevier, vol. 333(C).

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