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Data-driven optimization of nano-PCM arrangements for battery thermal management based on Lattice Boltzmann simulation

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  • Zhang, Liwei
  • Shang, Bichen
  • Sun, Weijie
  • Tao, Yao
  • Li, Xueren
  • Tu, Jiyuan

Abstract

An efficient Battery Thermal Management System (BTMS) is vital for maximizing electric vehicle effectiveness and extending service life, essential for sustainable transportation. This study proposes a novel non-uniform nano-PCM distribution strategy within BTMS to tackle battery overheating challenges and further proposes a multi-objective optimization framework combining Back Propagation Neural Networks (BPNN) and Genetic Algorithm (GA) to achieve optimal design solutions for BTMS. Optimization data is derived from the well-validated Lattice Boltzmann Method (LBM) results across 343 cases. Initial evaluations show that a negative gradient distributed nano-PCM (Type 2) improves melting rate, heat dissipation power, and temperature uniformity by 4.67%, 4.87%, 19%, and 7.0%, respectively. The BPNN-GA optimization framework satisfactorily correlates nanoparticle distribution with four evaluation metrics, achieving R2 values from 0.9469 to 0.9987. Optimization improves melting rate, heat dissipation power, and regional and inter-regional temperature uniformity by 9.13%, 9.94%, 7.77%, and 29.73%, respectively. The BPNN-GA also demonstrates reasonable generalizability for the other two practical case scenarios with improvements in certain criteria up to 49.19%. This study highlights the potential of uneven nano-PCM configurations and the efficiency of the LBM-BPNN-GA framework in achieving superior thermal management for BTMS, which is expected to provide insights for future BTMS designs and implementations.

Suggested Citation

  • Zhang, Liwei & Shang, Bichen & Sun, Weijie & Tao, Yao & Li, Xueren & Tu, Jiyuan, 2024. "Data-driven optimization of nano-PCM arrangements for battery thermal management based on Lattice Boltzmann simulation," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224034480
    DOI: 10.1016/j.energy.2024.133670
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    References listed on IDEAS

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    1. Guo, Junfei & Gao, Xinyu & Gao, Jiayi & Xie, Yuan & Yang, Xiaohu & He, Ya-Ling, 2026. "Thermal analysis and performance prediction on ultrasonic-assisted phase change thermal management device," Applied Energy, Elsevier, vol. 402(PB).
    2. Shang, Bichen & Li, Guo & Sun, Weijie & Zhang, Liwei & Cui, Guanzhe & Tu, Jiyuan & Fang, Xiang & Li, Xueren, 2025. "In-context learning for nano-PCM thermal behavior prediction in battery thermal management via Lattice Boltzmann simulation," Energy, Elsevier, vol. 338(C).

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