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Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model

Author

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  • Xiaoyu Li

    (College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
    National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Tengyuan Wang

    (College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Jiaxu Li

    (College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Yong Tian

    (College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China)

  • Jindong Tian

    (College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
    Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen University, Shenzhen 518060, China)

Abstract

The energy consumption of electric vehicles is closely related to the problems of charging station planning and vehicle route optimization. However, due to various factors, such as vehicle performance, driving habits and environmental conditions, it is difficult to estimate vehicle energy consumption accurately. In this work, a physical and data-driven fusion model was designed for electric bus energy consumption estimation. The basic energy consumption of the electric bus was modeled by a simplified physical model. The effects of rolling drag, brake consumption and air-conditioning consumption are considered in the model. Taking into account the fluctuation in energy consumption caused by multiple factors, a CatBoost decision tree model was constructed. Finally, a fusion model was built. Based on the analysis of electric bus data on the big data platform, the performance of the energy consumption model was verified. The results show that the model has high accuracy with an average relative error of 6.1%. The fusion model provides a powerful tool for the optimization of the energy consumption of electric buses, vehicle scheduling and the rational layout of charging facilities.

Suggested Citation

  • Xiaoyu Li & Tengyuan Wang & Jiaxu Li & Yong Tian & Jindong Tian, 2022. "Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model," Energies, MDPI, vol. 15(11), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4160-:d:832328
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    References listed on IDEAS

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    1. Yiwen Zhou & Fengxiang Guo & Simin Wu & Wenyao He & Xuefei Xiong & Zheng Chen & Dingan Ni, 2022. "Safety and Economic Evaluations of Electric Public Buses Based on Driving Behavior," Sustainability, MDPI, vol. 14(17), pages 1-17, August.
    2. Xiaoyu Li & Chuxin Wu & Chen Fu & Shanpu Zheng & Jindong Tian, 2022. "State Characterization of Lithium-Ion Battery Based on Ultrasonic Guided Wave Scanning," Energies, MDPI, vol. 15(16), pages 1-19, August.
    3. Zbigniew Czapla & Grzegorz Sierpiński, 2023. "Driving and Energy Profiles of Urban Bus Routes Predicted for Operation with Battery Electric Buses," Energies, MDPI, vol. 16(15), pages 1-19, July.

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