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Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios

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  • Wang, Siyang
  • Lin, Xianke

Abstract

This paper proposes a bi-level eco-driving control strategy for connected and automated hybrid electric vehicles (CAHEVs) under mixed driving scenarios. First, the hybrid electric vehicle powertrain is modelled, and the communications via Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) are introduced as the main data sources for the decision-making of the control system. Next, the problem is divided into three objectives, namely, (1) safe driving, (2) energy management, and (3) exhaust emission reduction. Based on the real-time road information, the driving scenario classifier (DSC) works towards determining the corresponding vehicle mode on which the cost function can be adjusted accordingly. The simulation is carried out in a realistic urban traffic simulation environment in SUMO. The results show that with the proposed model predictive control (MPC)-based strategy applied, safe driving in a trip involving a mixture of driving scenarios can be guaranteed throughout the entire driving. In addition, in comparison to the rule-based benchmark strategy, the proposed strategy can reduce the fuel consumption by 34.10% with battery kept in a healthy state of charge range, and the exhaust emissions (HC, CO, and NOx) are reduced by 25.36%, 72.30%, and 30.39%, respectively, which demonstrates the effectiveness and robustness of the proposed MPC-based strategy for CAHEVs.

Suggested Citation

  • Wang, Siyang & Lin, Xianke, 2020. "Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios," Applied Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:appene:v:271:y:2020:i:c:s0306261920307455
    DOI: 10.1016/j.apenergy.2020.115233
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    2. Pietro Stabile & Federico Ballo & Giorgio Previati & Giampiero Mastinu & Massimiliano Gobbi, 2023. "Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios," Energies, MDPI, vol. 16(3), pages 1-19, January.
    3. Luca Pulvirenti & Luigi Tresca & Luciano Rolando & Federico Millo, 2023. "Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario," Energies, MDPI, vol. 16(10), pages 1-19, May.
    4. Chen, Jie & Hu, Maobin & Shi, Congling, 2023. "Development of eco-routing guidance for connected electric vehicles in urban traffic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    5. 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).
    6. Nie, Zhigen & Jia, Yuan & Wang, Wanqiong & Chen, Zheng & Outbib, Rachid, 2022. "Co-optimization of speed planning and energy management for intelligent fuel cell hybrid vehicle considering complex traffic conditions," Energy, Elsevier, vol. 247(C).
    7. Liu, Rui & Liu, Hui & Han, Lijin & Nie, Shida & Ruan, Shumin & Yang, Ningkang, 2023. "Predictive eco-driving strategy for hybrid electric vehicles on off-road terrain considering vehicle stability constraint," Applied Energy, Elsevier, vol. 350(C).
    8. Liu, Rui & Liu, Hui & Nie, Shida & Han, Lijin & Yang, Ningkang, 2023. "A hierarchical eco-driving strategy for hybrid electric vehicles via vehicle-to-cloud connectivity," Energy, Elsevier, vol. 281(C).
    9. Simin Hesami & Majid Vafaeipour & Cedric De Cauwer & Evy Rombaut & Lieselot Vanhaverbeke & Thierry Coosemans, 2023. "Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility," Energies, MDPI, vol. 16(18), pages 1-19, September.
    10. Li, Cheng & Xu, Xiangyang & Zhu, Helong & Gan, Jiongpeng & Chen, Zhige & Tang, Xiaolin, 2024. "Research on car-following control and energy management strategy of hybrid electric vehicles in connected scene," Energy, Elsevier, vol. 293(C).
    11. 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).
    12. Hou, Zhuoran & Guo, Jianhua & Li, Jihao & Hu, Jinchen & Sun, Wen & Zhang, Yuanjian, 2023. "Exploration the pathways of connected electric vehicle design: A vehicle-environment cooperation energy management strategy," Energy, Elsevier, vol. 271(C).
    13. Haochen Xu & Niaona Zhang & Zonghao Li & Zichang Zhuo & Ye Zhang & Yilei Zhang & Haitao Ding, 2023. "Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    14. Alessia Musa & Michele Pipicelli & Matteo Spano & Francesco Tufano & Francesco De Nola & Gabriele Di Blasio & Alfredo Gimelli & Daniela Anna Misul & Gianluca Toscano, 2021. "A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
    15. Li, Bin & Dong, Xujun & Wen, Jianghui, 2022. "Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour," Energy, Elsevier, vol. 243(C).
    16. Chen, Zheng & Wu, Simin & Shen, Shiquan & Liu, Yonggang & Guo, Fengxiang & Zhang, Yuanjian, 2023. "Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios," Energy, Elsevier, vol. 263(PF).

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