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An eco-driving strategy for autonomous electric vehicles crossing continuous speed-limit signalized intersections

Author

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  • Liu, Jinqiang
  • Wang, Chunyan
  • Zhao, Wanzhong

Abstract

The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods.

Suggested Citation

  • Liu, Jinqiang & Wang, Chunyan & Zhao, Wanzhong, 2024. "An eco-driving strategy for autonomous electric vehicles crossing continuous speed-limit signalized intersections," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224006017
    DOI: 10.1016/j.energy.2024.130829
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    References listed on IDEAS

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    1. Li, Jie & Fotouhi, Abbas & Pan, Wenjun & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng, 2023. "Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties," Energy, Elsevier, vol. 279(C).
    2. Lee, Heeyun & Kim, Kyunghyun & Kim, Namwook & Cha, Suk Won, 2022. "Energy efficient speed planning of electric vehicles for car-following scenario using model-based reinforcement learning," Applied Energy, Elsevier, vol. 313(C).
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    4. 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).
    5. Liu, Yonggang & Huang, Bin & Yang, Yang & Lei, Zhenzhen & Zhang, Yuanjian & Chen, Zheng, 2022. "Hierarchical speed planning and energy management for autonomous plug-in hybrid electric vehicle in vehicle-following environment," Energy, Elsevier, vol. 260(C).
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    Cited by:

    1. Li, Yuan & Pan, Chaofeng & Wang, Jian & Li, Zhongxing & Liang, Jun & Cai, Chongyu, 2024. "Research on energy consumption optimization of predictive cruise control considering the state of the leading vehicle," Energy, Elsevier, vol. 308(C).
    2. Gongquan Zhang & Fangrong Chang & Helai Huang & Zilong Zhou, 2024. "Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization," Mathematics, MDPI, vol. 12(13), pages 1-24, June.
    3. Wang, Yue & Li, Weiliang & Wu, Jingda & Gao, Bolin & Zhong, Wei & He, Lei & Lv, Chen & He, Hongwen & Li, Keqiang, 2025. "Interaction-aware eco-driving control under complex traffic environment: A comprehensive review," Applied Energy, Elsevier, vol. 401(PB).
    4. Li, Jie & Liu, Yonggang & Cheng, Jun & Fotouhi, Abbas & Chen, Zheng, 2024. "Eco-driving control for connected plug-in hybrid electric vehicles in urban scenarios with enhanced lane change engagement," Energy, Elsevier, vol. 310(C).
    5. Huang, Jianchang & Wang, Xin & Lin, Qinghai & Song, Guohua & Yu, Lei, 2025. "Unraveling inter-driver and intra-driver uncertainty: An eco-driving evaluation and optimization method," Energy, Elsevier, vol. 321(C).

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