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Longitudinal autonomous driving based on game theory for intelligent hybrid electric vehicles with connectivity

Author

Listed:
  • Cheng, Shuo
  • Li, Liang
  • Chen, Xiang
  • Fang, Sheng-nan
  • Wang, Xiang-yu
  • Wu, Xiu-heng
  • Li, Wei-bing

Abstract

Autonomous driving hybrid electric vehicles can offer unprecedented opportunities for autonomous safe & energy-efficient driving. However, how to integrate energy optimization during the car-following process and vehicle safety under complex traffic flow is a formidable challenge. Moreover, the coordinated control of three chassis parts including electric motor, internal combustion engine and vehicle brake system is hard to be tackled. Therefore, this paper aims to address longitudinal autonomous driving for intelligent hybrid electric vehicles. A game-theory-based longitudinal autonomous driving control framework is proposed with much easier access to information due to vehicle-to-vehicle/vehicle-to-infrastructure communication, which is our main contribution. Firstly, the whole longitudinal driving control is transformed into a multi-objective optimal problem, which contains safety, economy, comfort, so a game theory model is built to solve the multi-objective equilibrium problem. Then, to obtain the closed-loop strategies in Nash differential game, a system of coupled algebraic Riccati equations is solved. Finally, the game-theory-based control strategies coordinate electric motor, internal combustion engine and vehicle brake system to achieve multi-objective equilibrium. Simulation tests of the proposed framework and previous existing work are carried out, and their results show the proposed framework’s better performance of longitudinal dynamics control including car-following, reducing fuel consumption, and driving comfort.

Suggested Citation

  • Cheng, Shuo & Li, Liang & Chen, Xiang & Fang, Sheng-nan & Wang, Xiang-yu & Wu, Xiu-heng & Li, Wei-bing, 2020. "Longitudinal autonomous driving based on game theory for intelligent hybrid electric vehicles with connectivity," Applied Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:appene:v:268:y:2020:i:c:s0306261920305420
    DOI: 10.1016/j.apenergy.2020.115030
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Sueyoshi, Fumi & Utsumi, Shinobu & Tanimoto, Jun, 2022. "Underlying social dilemmas in mixed traffic flow with lane changes," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Lin, Xinyou & Li, Yalong & Zhang, Guangji, 2022. "Bi-objective optimization strategy of energy consumption and shift shock based driving cycle-aware bias coefficients for a novel dual-motor electric vehicle," Energy, Elsevier, vol. 249(C).
    3. Jiang, Yue & Meng, Hao & Chen, Guanpeng & Yang, Congnan & Xu, Xiaojun & Zhang, Lei & Xu, Haijun, 2022. "Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle," Energy, Elsevier, vol. 254(PA).
    4. Zhang, Bo & Zhang, Jiangyan & Shen, Tielong, 2022. "Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode," Applied Energy, Elsevier, vol. 305(C).
    5. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).
    6. Ruan, Shumin & Ma, Yue & Yang, Ningkang & Yan, Qi & Xiang, Changle, 2023. "Multiobjective optimization of longitudinal dynamics and energy management for HEVs based on nash bargaining game," Energy, Elsevier, vol. 262(PA).
    7. 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).
    8. Ruan, Shumin & Ma, Yue & Yang, Ningkang & Xiang, Changle & Li, Xunming, 2022. "Real-time energy-saving control for HEVs in car-following scenario with a double explicit MPC approach," Energy, Elsevier, vol. 247(C).

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