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Modeling, implementation and experimental verification of eco-driving on a battery-electric heavy-duty vehicle

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  • Heuts, Y.J.J.
  • Wouters, J.J.F.
  • Hulsebos, O.F.
  • Donkers, M.C.F.

Abstract

In this paper, an Eco-Driving Assistance System (EDAS) has been implemented on a fully electric heavy-duty vehicle and its performance has been validated using real-world experiments. The objective of the EDAS is to provide the driver with a recommendation on the vehicle’s optimal speed trajectory that minimizes its energy consumption over the entire trip. This requires solving a receding horizon optimal control problem, which, in this case, consists of a convex optimization problem and can be solved as a second-order cone program. Simulations were used to explore different prediction horizon lengths and move-blocking strategies of the underlying receding horizon optimal control problem, aiming to strike a balance between numerical complexity and energy savings. Finally, the method is implemented on an electric heavy-duty vehicle where an augmented speedometer is presented to the driver. Multiple tests with and without an EDAS have been performed, which resulted in a reduction of 6.5 %–12 % in energy consumption compared to when the vehicle was driven without the EDAS active.

Suggested Citation

  • Heuts, Y.J.J. & Wouters, J.J.F. & Hulsebos, O.F. & Donkers, M.C.F., 2025. "Modeling, implementation and experimental verification of eco-driving on a battery-electric heavy-duty vehicle," Applied Energy, Elsevier, vol. 390(C).
    • Handle: RePEc:eee:appene:v:390:y:2025:i:c:s0306261925005124
    DOI: 10.1016/j.apenergy.2025.125782
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    References listed on IDEAS

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    1. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    2. Zhuang, Weichao & Li, Jinhui & Ju, Fei & Li, Bingbing & Liu, Haoji & Yin, Guodong, 2024. "Dual-objective eco-routing strategy for vehicles with different powertrain types," Energy, Elsevier, vol. 293(C).
    3. 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).
    4. Haubensak, Lukas & Strahl, Stephan & Braun, Jochen & Faulwasser, Timm, 2024. "Towards real-time capable optimal control for fuel cell vehicles using hierarchical economic MPC," Applied Energy, Elsevier, vol. 366(C).
    5. Liao, Peng & Tang, Tie-Qiao & Liu, Ronghui & Huang, Hai-Jun, 2021. "An eco-driving strategy for electric vehicle based on the powertrain," Applied Energy, Elsevier, vol. 302(C).
    6. Zou, Weitao & Li, Jianwei & Yang, Qingqing & Wan, Xinming & He, Yuntang & Lan, Hao, 2023. "A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle," Applied Energy, Elsevier, vol. 334(C).
    7. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    8. Wu, Yue & Huang, Zhiwu & Li, Dongjun & Li, Heng & Peng, Jun & Stroe, Daniel & Song, Ziyou, 2024. "Optimal battery thermal management for electric vehicles with battery degradation minimization," Applied Energy, Elsevier, vol. 353(PA).
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    1. Heuts, Y.J.J. & Velpari, R. & Donkers, M.C.F., 2025. "Eco-driving and road curvature estimation: Retrospective analysis of experimental data of a fully electric coach," Energy, Elsevier, vol. 334(C).
    2. Wang, Chuang & Yang, Zhensen & Zhu, Lijun & Zhang, Lijun, 2025. "Learning-augmented hierarchical control for signal-aware safe eco-driving of connected autonomous vehicles," Applied Energy, Elsevier, vol. 401(PC).

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