Accelerated computational strategies for multi-scale thermal runaway prediction models in Li-ion battery

Pengfei Zhang, Haipeng Chen, Kangbo Yang, Yiji Lu and Yuqi Huang

Energy, 2024, vol. 305, issue C

Abstract: Numerical simulation techniques have become an important tool in the study of thermal runaway (TR) in lithium-ion batteries (LIB). With the research progress, the complexity of the TR prediction model increases, extending its application from single battery to battery packs and entire vehicle systems. This expansion has led to a significant increase in the computational demands of TR prediction model. The computational efficiency has become a critical issue that cannot be ignored. Therefore, it is meaningful to research and develop accelerated computational strategies for the TR prediction model while ensuring the computational accuracy and stability. This paper introduces specific accelerated computational strategies to tackle this challenge. The stability and accuracy of these strategies are validated using 2D and 3D models developed with OpenFOAM software, and the speed up effect from coupling of different accelerated schemes is also analysed. The results show that, compared with the traditional prediction model, coupling various accelerated strategies can speed up the 2D model by a factor of 4.91x and 3D model by a factor of 6.07x. The optimized models with accelerated computational strategies substantially improve the ability to handle complex working conditions, large-scale models and TR propagation problems.

Keywords: TR prediction model; Gas-solid flow; Accelerated computational strategies (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:305:y:2024:i:c:s0360544224021455

DOI: 10.1016/j.energy.2024.132371

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