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Eco-driving control for connected plug-in hybrid electric vehicles in urban scenarios with enhanced lane change engagement

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  • Li, Jie
  • Liu, Yonggang
  • Cheng, Jun
  • Fotouhi, Abbas
  • Chen, Zheng

Abstract

Eco-driving control techniques have shown significant potential in reducing energy consumption in urban scenarios. The presence of slow-moving vehicles typically disrupts ecological velocity planning, leading to an increase in energy consumption. To solve it, this study proposes a hierarchical eco-driving control strategy, that integrates speed optimization and lane change decision-making in urban scenarios, to not only ensure traffic efficiency, but also save the energy consumption. Firstly, a data-driven energy model is leveraged in the upper level to estimate the energy consumption of candidate lanes and generate ecological lane change decisions. Then, in the lower level, the preceding vehicles and traffic lights are considered to plan an ecological velocity profile via deep reinforcement learning algorithm after transitions to the target driving lane, thereby enhancing the fuel economy and travel efficiency. A virtual driving environment model is established to verify the proposed method through numerous simulation cases. The results indicate that the proposed method effectively reduces energy consumption while maintaining favorable travel efficiency, compared with conventional benchmarks. Furthermore, the notable improvements are observed particularly in free traffic conditions.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:310:y:2024:i:c:s0360544224030706
    DOI: 10.1016/j.energy.2024.133294
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    References listed on IDEAS

    as
    1. 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).
    2. Wang, Siyang & Lin, Xianke, 2020. "Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios," Applied Energy, Elsevier, vol. 271(C).
    3. 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).
    4. Yao, Zhihong & Jin, Yuting & Jiang, Haoran & Hu, Lu & Jiang, Yangsheng, 2022. "CTM-based traffic signal optimization of mixed traffic flow with connected automated vehicles and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Shi, Dehua & Xu, Han & Wang, Shaohua & Hu, Jia & Chen, Long & Yin, Chunfang, 2024. "Deep reinforcement learning based adaptive energy management for plug-in hybrid electric vehicle with double deep Q-network," Energy, Elsevier, vol. 305(C).
    6. 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).
    7. 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).
    8. Zhang, Chuntao & Huang, Wenhui & Zhou, Xingyu & Lv, Chen & Sun, Chao, 2024. "Expert-demonstration-augmented reinforcement learning for lane-change-aware eco-driving traversing consecutive traffic lights," Energy, Elsevier, vol. 286(C).
    9. Wang, Shaohua & Zhang, Kaimei & Shi, Dehua & Li, Meng & Yin, Chunfang, 2024. "Research on economical shifting strategy for multi-gear and multi-mode parallel plug-in HEV based on DIRECT algorithm," Energy, Elsevier, vol. 286(C).
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