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Energy efficient speed planning of electric vehicles for car-following scenario using model-based reinforcement learning

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  • Lee, Heeyun
  • Kim, Kyunghyun
  • Kim, Namwook
  • Cha, Suk Won

Abstract

Eco-driving is a term used to refer to a strategy for operating vehicles so as to minimize energy consumption. Without any hardware changes, eco-driving is an effective approach to improving vehicle efficiency by optimizing driving behavior, particularly for autonomous vehicles. Several approaches have been proposed for eco-driving, such as dynamic programming, Pontryagin’s minimum principle, and model predictive control; however, it is difficult to control the speed of the vehicle optimally in various driving situations. This study aims to derive an eco-driving strategy for reducing the energy consumption of a vehicle in diverse driving situations, including road slopes and car-following scenarios. A reinforcement learning-based energy efficient speed planning strategy is proposed for autonomous electric vehicles, which learn an optimal control policy through a data-driven learning process. A model-based reinforcement learning algorithm is developed for the eco-driving strategy; based on domain knowledge of the vehicle powertrain, a battery energy consumption model and longitudinal dynamics model of the vehicle are approximated from the driving data and are used for reinforcement learning. The proposed algorithm is tested using a vehicle simulation, and is compared to a global optimal solution obtained using an exact dynamic programming method. The simulation results show that the reinforcement learning algorithm can adjust the speed of the vehicle by considering driving conditions such as the road slope and a safe distance from the leading vehicle while minimizing energy consumption. The reinforcement learning algorithm achieves a near-optimal performance of 93.8% relative to the dynamic programming result.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:313:y:2022:i:c:s0306261921016858
    DOI: 10.1016/j.apenergy.2021.118460
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    References listed on IDEAS

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    1. Barkenbus, Jack N., 2010. "Eco-driving: An overlooked climate change initiative," Energy Policy, Elsevier, vol. 38(2), pages 762-769, February.
    2. Xi, Jiaqi & Li, Mian & Xu, Min, 2014. "Optimal energy management strategy for battery powered electric vehicles," Applied Energy, Elsevier, vol. 134(C), pages 332-341.
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    Citations

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    Cited by:

    1. Wang, Yong & Wu, Yuankai & Tang, Yingjuan & Li, Qin & He, Hongwen, 2023. "Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 332(C).
    2. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    3. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    4. Shuang Jin & Jianxi Yang & Zhongcheng Liu, 2022. "Modeling and Analysis of Car-Following for Intelligent Connected Vehicles Considering Expected Speed in Helical Ramps," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    5. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    6. Li, Jie & Fotouhi, Abbas & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng, 2024. "Review on eco-driving control for connected and automated vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    7. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.
    8. Carlo Fiorillo & Mattia Mauro & Atriya Biswas & Angelo Bonfitto & Ali Emadi, 2024. "Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication," Energies, MDPI, vol. 17(9), pages 1-18, April.
    9. 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).
    10. Wenya Xu & Yanxue Li & Guanjie He & Yang Xu & Weijun Gao, 2023. "Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control," Energies, MDPI, vol. 16(13), pages 1-19, June.
    11. 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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