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Enhanced Thermal Modeling of Electric Vehicle Motors Using a Multihead Attention Mechanism

Author

Listed:
  • Feifan Ji

    (School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China
    These authors contributed equally to this work.)

  • Chenglong Huang

    (Zhejiang Leapmotor Technology Co., Ltd., Hangzhou 310000, China
    These authors contributed equally to this work.)

  • Tong Wang

    (School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China
    These authors contributed equally to this work.)

  • Yanjun Li

    (School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China)

  • Shuwen Pan

    (School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China)

Abstract

The rapid advancement of electric vehicles (EVs) accentuates the criticality of efficient thermal management systems for electric motors, which are pivotal for performance, reliability, and longevity. Traditional thermal modeling techniques often struggle with the dynamic and complex nature of EV operations, leading to inaccuracies in temperature prediction and management. This study introduces a novel thermal modeling approach that utilizes a multihead attention mechanism, aiming to significantly enhance the prediction accuracy of motor temperature under varying operational conditions. Through meticulous feature engineering and the deployment of advanced data handling techniques, we developed a model that adeptly navigates the intricacies of temperature fluctuations, thereby contributing to the optimization of EV performance and reliability. Our evaluation using a comprehensive dataset encompassing temperature data from 100 electric vehicles illustrates our model’s superior predictive performance, notably improving temperature prediction accuracy.

Suggested Citation

  • Feifan Ji & Chenglong Huang & Tong Wang & Yanjun Li & Shuwen Pan, 2024. "Enhanced Thermal Modeling of Electric Vehicle Motors Using a Multihead Attention Mechanism," Energies, MDPI, vol. 17(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2976-:d:1416239
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    References listed on IDEAS

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    1. Wan, Zijing & Wei, Fulong & Peng, Jiale & Deng, Chao & Ding, Siqi & Xu, Dongwei & Luo, Xiaobing, 2023. "Application of physical model-based machine learning to the temperature prediction of electronic device in oil-gas exploration logging," Energy, Elsevier, vol. 282(C).
    2. Zhang, Xinghui & Li, Zhao & Luo, Lingai & Fan, Yilin & Du, Zhengyu, 2022. "A review on thermal management of lithium-ion batteries for electric vehicles," Energy, Elsevier, vol. 238(PA).
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    Cited by:

    1. Samuel Moveh & Emmanuel Alejandro Merchán-Cruz & Maher Abuhussain & Yakubu Aminu Dodo & Saleh Alhumaid & Ali Hussain Alhamami, 2025. "Deep Learning Framework Using Transformer Networks for Multi Building Energy Consumption Prediction in Smart Cities," Energies, MDPI, vol. 18(6), pages 1-22, March.

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