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Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios

Author

Listed:
  • Haochen Xu

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)

  • Niaona Zhang

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China
    State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130025, China)

  • Zonghao Li

    (State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130025, China)

  • Zichang Zhuo

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)

  • Ye Zhang

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)

  • Yilei Zhang

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)

  • Haitao Ding

    (State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130025, China)

Abstract

Eco-driving is a driving vehicle strategy aimed at minimizing energy consumption; that is, it is a method to improve vehicle efficiency by optimizing driving behavior without making any hardware changes, especially for autonomous vehicles. To enhance energy efficiency across various driving scenarios, including road slopes, car following scenarios, and traffic signal interactions, this research introduces an energy-conserving speed planning approach for self-driving electric vehicles employing reinforcement learning. This strategy leverages vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to acquire real-time data regarding traffic signal timing, leading vehicle speeds, and other pertinent driving conditions. In the framework of rolling horizon reinforcement learning (RHRL), predictions are made in each window using a rolling time domain approach. In the evaluation stage, Q-learning is used to obtain the optimal evaluation value, so that the vehicle can reach a reasonable speed. In conclusion, the algorithm’s efficacy is confirmed through vehicle simulation, with the results demonstrating that reinforcement learning adeptly modulates vehicle speed to minimize energy consumption, all while taking into account factors like road grade and maintaining a secure following distance from the preceding vehicle. Compared with the results of traditional adaptive cruise control (ACC), the algorithm can save 11.66% and 30.67% of energy under two working conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15947-:d:1280118
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    References listed on IDEAS

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    1. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2016. "Modelling the multilevel structure and mixed effects of the factors influencing the energy consumption of electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1351-1360.
    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. Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo, 2022. "Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle," Energy, Elsevier, vol. 238(PC).
    4. Chen, Shuang & Hu, Minghui & Guo, Shanqi, 2023. "Fast dynamic-programming algorithm for solving global optimization problems of hybrid electric vehicles," Energy, Elsevier, vol. 273(C).
    5. Xiong, Xi & Sha, Junyi & Jin, Li, 2021. "Optimizing coordinated vehicle platooning: An analytical approach based on stochastic dynamic programming," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 482-502.
    6. Zhao, Xin & Doering, Otto C. & Tyner, Wallace E., 2015. "The economic competitiveness and emissions of battery electric vehicles in China," Applied Energy, Elsevier, vol. 156(C), pages 666-675.
    7. Wu, Yue & Huang, Zhiwu & Hofmann, Heath & Liu, Yongjie & Huang, Jiahao & Hu, Xiaosong & Peng, Jun & Song, Ziyou, 2022. "Hierarchical predictive control for electric vehicles with hybrid energy storage system under vehicle-following scenarios," Energy, Elsevier, vol. 251(C).
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