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Thermal Management Strategy for Electric Vehicle Fast Charging Module Based on Predictive Control
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Jingxuan LI, Yansong LU, Chong ZHU, Xu LU, Xi ZHANG
Journal of Power Supply | 2025, 23(2) : 240 - 246
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Journal of Power Supply | 2025, 23(2): 240-246
• Battery and Energy Storage •
Thermal Management Strategy for Electric Vehicle Fast Charging Module Based on Predictive Control
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Jingxuan LI, Yansong LU, Chong ZHU, Xu LU, Xi ZHANG
Affiliations
  • School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Published: 2025-03-30 doi: 10.13234/j.issn.2095-2805.2025.2.240
Outline
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Electric vehicle fast charging piles are prone to overheating of power devices under high-power operation, causing potential safety hazards. However, the existing cooling strategy adopts a rule-based forced air cooling method, and the cooling fan rotates at a high speed and generates large environmental noise. To protect the thermal safety of core components in the module while optimizing the cooling regulation strategy, an optimal thermal management method for electric vehicle fast charging module based on data-driven model predictive control (MPC) is proposed. This method adopts a data-driven method to construct a prediction model of module temperature distribution based on the long short-term memory neural network, and it combines MPC to control the fan speed, thus optimizing the thermal management strategy for the fast charging module and reducing the fan noise. Through experimental tests, it was verified that this method can effectively reduce the average fan speed by 1 293 rpm and reduce the average noise by 4.99 dB while ensuring that the key components are not overheated, which ensures the thermal safety of core components and the durability of the cooling fan.

Model predictive control (MPC)  /  long short-term memory neural network  /  fast charging module  /  thermal management  /  fan noise reduction
Jingxuan LI, Yansong LU, Chong ZHU, Xu LU, Xi ZHANG. Thermal Management Strategy for Electric Vehicle Fast Charging Module Based on Predictive Control[J]. Journal of Power Supply, 2025 , 23 (2) : 240 -246 . DOI: 10.13234/j.issn.2095-2805.2025.2.240
Funding
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  • National Natural Science Foundation of China(52177218)
  • National Natural Science Foundation of China(52007119)
  • National Key R&D Plan Key Special Project(2019YFE0100200)
Appendix
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Year 2025 volume 23 Issue 2
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Article Info
doi: 10.13234/j.issn.2095-2805.2025.2.240
  • Receive Date：2022-07-04
  • Online Date：2025-07-01
  • Published：2025-03-30
Article Data
Affiliations
History
  • Received：2022-07-04
  • Revised：2022-09-25
  • Accepted：2022-10-11
Funding
National Natural Science Foundation of China(52177218)
National Natural Science Foundation of China(52007119)
National Key R&D Plan Key Special Project(2019YFE0100200)
Affiliations
    School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
References
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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