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Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model

Author

Listed:
  • Qian Zhang

    (School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China
    School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China)

  • Shaopeng Tian

    (School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
    Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
    Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China)

Abstract

The development of hybrid technology can effectively solve the problems of the high pollution and energy consumption levels of automobiles. Therefore, an energy consumption prediction and control algorithm for hybrid vehicles based on a minimum equivalent fuel consumption model is proposed. The model’s battery power consumption is equivalent to the fuel consumption, and the sum of the engine fuel consumption and the battery equivalent fuel consumption is established as the objective function. By utilizing these factors, an innovative minimum equivalent fuel consumption model was constructed that could be used to measure the energy efficiency of hybrid vehicles. The longitudinal force result of braking force distribution control was obtained, as well as the energy consumption prediction structure of a hybrid electric vehicle. The rolling resistance, air resistance, and climbing resistance of the hybrid electric vehicles were calculated, and the energy consumption control algorithm for hybrid electric vehicles was constructed according to the calculation results. The experimental results indicated that under this research algorithm, the driving energy consumption of hybrid electric vehicles was relatively low and the energy consumption and energy efficiency measurements effectively met the actual demand, and the energy consumption prediction and control results were good.

Suggested Citation

  • Qian Zhang & Shaopeng Tian, 2023. "Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9394-:d:1168775
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    References listed on IDEAS

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    Cited by:

    1. Witsarut Achariyaviriya & Wongkot Wongsapai & Kittitat Janpoom & Tossapon Katongtung & Yuttana Mona & Nakorn Tippayawong & Pana Suttakul, 2023. "Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches," Energies, MDPI, vol. 16(17), pages 1-14, September.
    2. Huang, Haichao & Gao, Kun & Wang, Yizhou & Najafi, Arsalan & Zhang, Zhe & He, Hongdi, 2025. "Sequence-aware energy consumption prediction for electric vehicles using pre-trip realistically accessible data," Applied Energy, Elsevier, vol. 401(PA).
    3. Aminu Babangida & Péter Tamás Szemes, 2024. "Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain," Energies, MDPI, vol. 17(18), pages 1-38, September.
    4. Runze Liu & Jianming Cai & Lipeng Hu & Benxiao Lou & Jinjun Tang, 2025. "Electric Bus Battery Energy Consumption Estimation and Influencing Features Analysis Using a Two-Layer Stacking Framework with SHAP-Based Interpretation," Sustainability, MDPI, vol. 17(15), pages 1-27, August.

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