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An eco-driving strategy for electric buses at signalized intersection with a bus stop based on energy consumption prediction

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  • Pan, Yingjiu
  • Xi, Yi
  • Fang, Wenpeng
  • Liu, Yansen
  • Zhang, Yali
  • Zhang, Wenshan

Abstract

The interaction between signalized intersections and downstream bus stops often leads to increased energy consumption in electric city buses. To address this issue, this study proposes a specialized eco-driving strategy tailored for electric buses operating at signalized intersections and bus stops. First, this study investigates how driving behavior affects energy consumption and develops a predictive model for energy consumption utilizing real-world driving data. Second, an optimized reward function is formulated based on the constructed energy consumption prediction model, incorporating considerations of safety, efficiency, and comfort. Subsequently, an acceleration and deceleration strategy are established using the Soft Actor-Critic framework to generate an eco-velocity curve. The effectiveness of this strategy is evaluated against real-world driving data. When compared to the driving behaviors observed in three distinct real-world scenarios, the proposed strategy demonstrates energy savings of 31.19 %, 20.84 %, and 30.29 % for electric buses navigating signalized intersections and bus stops continuously.

Suggested Citation

  • Pan, Yingjiu & Xi, Yi & Fang, Wenpeng & Liu, Yansen & Zhang, Yali & Zhang, Wenshan, 2025. "An eco-driving strategy for electric buses at signalized intersection with a bus stop based on energy consumption prediction," Energy, Elsevier, vol. 317(C).
  • Handle: RePEc:eee:energy:v:317:y:2025:i:c:s0360544225003147
    DOI: 10.1016/j.energy.2025.134672
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    References listed on IDEAS

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    1. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    2. Li, Jie & Wu, Xiaodong & Fan, Jiawei & Liu, Yonggang & Xu, Min, 2023. "Overcoming driving challenges in complex urban traffic: A multi-objective eco-driving strategy via safety model based reinforcement learning," Energy, Elsevier, vol. 284(C).
    3. Dong, Haoxuan & Zhuang, Weichao & Chen, Boli & Wang, Yan & Lu, Yanbo & Liu, Ying & Xu, Liwei & Yin, Guodong, 2022. "A comparative study of energy-efficient driving strategy for connected internal combustion engine and electric vehicles at signalized intersections," Applied Energy, Elsevier, vol. 310(C).
    4. Pan, Yingjiu & Fang, Wenpeng & Ge, Zhenzhen & Li, Cheng & Wang, Caifeng & Guo, Baochang, 2024. "A hybrid on-line approach for predicting the energy consumption of electric buses based on vehicle dynamics and system identification," Energy, Elsevier, vol. 290(C).
    5. Li, Jie & Wu, Xiaodong & Xu, Min & Liu, Yonggang, 2022. "Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections," Energy, Elsevier, vol. 251(C).
    6. Qu, Xiaobo & Yu, Yang & Zhou, Mofan & Lin, Chin-Teng & Wang, Xiangyu, 2020. "Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach," Applied Energy, Elsevier, vol. 257(C).
    7. Liu, Chunyu & Sheng, Zihao & Chen, Sikai & Shi, Haotian & Ran, Bin, 2023. "Longitudinal control of connected and automated vehicles among signalized intersections in mixed traffic flow with deep reinforcement learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
    Full references (including those not matched with items on IDEAS)

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