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Optimal Model of Electric Bus Scheduling Based on Energy Consumption and Battery Loss

Author

Listed:
  • Yan Xing

    (School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China)

  • Quanbo Fu

    (School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China)

  • Yachao Li

    (School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China)

  • Hanshuo Chu

    (School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China)

  • Enyi Niu

    (School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110168, China)

Abstract

The characteristics of electric buses make it difficult to estimate the energy consumption and mean that they are prone to battery loss; as such, fuel bus scheduling methods are no longer fully applicable. In current studies, the influence of these factors is ignored. This paper proposes an electric bus scheduling optimization model based on energy consumption and battery loss. Firstly, the LSTM (long short-term memory) is used to estimate trip energy consumption. Subsequently, these results are combined with the optimization objectives of minimizing the fleet size and battery loss amount. Limitations on the buses’ number, travel time, battery safety thresholds, remaining charge, and total charge are also considered. By controlling the different battery charge and discharge thresholds to minimize battery losses, the goal of sustainability is achieved. NSGA-II (non-dominated sorting genetic algorithm-II) is used to solve the model. The corresponding scheduling and charging scheme are determined. Electric bus route A is taken to validate the predictions. The results show that the annual fleet battery loss value decreases as the fleet size increases. The company has the lowest annual operating cost when the battery charge and discharge thresholds are set to [25%, 85%]. Optimizing the scheduling and charging scheme for electric bus can effectively reduce the operating cost.

Suggested Citation

  • Yan Xing & Quanbo Fu & Yachao Li & Hanshuo Chu & Enyi Niu, 2023. "Optimal Model of Electric Bus Scheduling Based on Energy Consumption and Battery Loss," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9640-:d:1172188
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    References listed on IDEAS

    as
    1. Yan Xing & Yachao Li & Weidong Liu & Wenqing Li & Lingxuan Meng & Min Ye, 2022. "Operation Energy Consumption Estimation Method of Electric Bus Based on CNN Time Series Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, October.
    2. Gkiotsalitis, K. & Iliopoulou, C. & Kepaptsoglou, K., 2023. "An exact approach for the multi-depot electric bus scheduling problem with time windows," European Journal of Operational Research, Elsevier, vol. 306(1), pages 189-206.
    3. Zhou, Yu & Meng, Qiang & Ong, Ghim Ping, 2022. "Electric Bus Charging Scheduling for a Single Public Transport Route Considering Nonlinear Charging Profile and Battery Degradation Effect," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 49-75.
    4. Basma, Hussein & Mansour, Charbel & Haddad, Marc & Nemer, Maroun & Stabat, Pascal, 2022. "Energy consumption and battery sizing for different types of electric bus service," Energy, Elsevier, vol. 239(PE).
    5. Zhang, Le & Wang, Shuaian & Qu, Xiaobo, 2021. "Optimal electric bus fleet scheduling considering battery degradation and non-linear charging profile," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    6. Tang, Chong & Liu, Mingbo & Xie, Min & Dong, Ping & Zhu, Jianquan & Lin, Shunjiang, 2021. "A single-leader and multiple-follower stackelberg model for the look-ahead dispatch of plug-in electric buses in multiple microgrids," Energy, Elsevier, vol. 214(C).
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