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Self-adapting J-type air-based battery thermal management system via model predictive control

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  • Liu, Yuanzhi
  • Zhang, Jie

Abstract

Battery thermal control plays an indispensable role in terms of the safety and performance for electric vehicles. For air-based cooling technologies, one of the most pressing challenges is to balance the temperature uniformity and constrain the maximum temperature simultaneously under varying driving conditions. This paper proposes a self-adaptive intelligent neural network-based model predictive control strategy for a J-type air-based battery thermal management system. The J-type structure is first optimized through surrogate-based optimization to improve the temperature uniformity before control. Based on the optimized J-type configuration, an operation mode switching module is developed to mitigate the temperature unbalance. The thermal control approach is tested using an integrated driving cycle, and its evaluations are threefold: (i) the neural network-based control without mode switching fails to meet the thermal requirements; (ii) the control with mode switching succeeds in constraining the maximum temperature and maintaining the temperature uniformity within 1.33 K; (iii) the added model predictive control approach slightly enhances the thermal performance but improves the energy efficiency significantly by 15.8%. The results show that the J-type structure with its appropriate control strategy is a promising solution for light-duty electric vehicles using an air-cooling technology.

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  • Liu, Yuanzhi & Zhang, Jie, 2020. "Self-adapting J-type air-based battery thermal management system via model predictive control," Applied Energy, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:appene:v:263:y:2020:i:c:s0306261920301525
    DOI: 10.1016/j.apenergy.2020.114640
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    5. Peizheng Li & Jiapei Zhao & Shuai Zhou & Jiabin Duan & Xinke Li & Houcheng Zhang & Jinliang Yuan, 2023. "Design and Optimization of a Liquid Cooling Thermal Management System with Flow Distributors and Spiral Channel Cooling Plates for Lithium-Ion Batteries," Energies, MDPI, vol. 16(5), pages 1-23, February.
    6. Fan Fei & Dong Wang, 2023. "Application of Neural Network Feedforward in Fuzzy PI Controller for Electric Vehicle Thermal Management System: Modeling and Simulation Studies," Energies, MDPI, vol. 17(1), pages 1-29, December.
    7. Chen, Quanyi & Zhang, Xuan & Nie, Pengbo & Zhang, Siwei & Wei, Guodan & Sun, Hongbin, 2023. "A fast thermal simulation and dynamic feedback control framework for lithium-ion batteries," Applied Energy, Elsevier, vol. 350(C).
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    9. Gang Zhao & Xiaolin Wang & Michael Negnevitsky & Hengyun Zhang & Chengjiang Li, 2022. "Performance Improvement of a Novel Trapezoid Air-Cooling Battery Thermal Management System for Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-21, April.
    10. Weng, Jingwen & Xiao, Changren & Ouyang, Dongxu & Yang, Xiaoqing & Chen, Mingyi & Zhang, Guoqing & Yuen, Richard Kwok Kit & Wang, Jian, 2022. "Mitigation effects on thermal runaway propagation of structure-enhanced phase change material modules with flame retardant additives," Energy, Elsevier, vol. 239(PC).
    11. Yang, Huizhu & Li, Mingxuan & Wang, Zehui & Ma, Binjian, 2023. "A compact and lightweight hybrid liquid cooling system coupling with Z-type cold plates and PCM composite for battery thermal management," Energy, Elsevier, vol. 263(PE).

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