IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v324y2025ics0360544225014355.html

Multiobjective eco-driving speed optimisation with real-time traffic: Balancing fuel, NOx, and travel time

Author

Listed:
  • Liu, Enze
  • Lin, Zhiyuan
  • Chen, Haibo
  • Jia, Dongyao
  • Liu, Ye
  • Guo, Junhua
  • Li, Tiezhu
  • Wei, Tangjian

Abstract

Optimising driving velocity profiles is crucial for reducing vehicle fuel consumption and NOx emissions without altering core vehicle components. While many studies have addressed eco-driving, most have focused solely on minimising fuel consumption or have treated NOx emissions separately, resulting in distinct, non-integrated speed profiles, and have often neglected the influence of real-time traffic. To overcome these limitations, this paper introduces a novel Multiobjective Speed Profile Optimisation (MO-SPO) framework for eco-driving that simultaneously minimises fuel consumption, NOx emissions, and travel time while accounting for surrounding traffic. Two solution approaches are developed and compared: a two-phase Model Predictive Control (MPC) method and a newly proposed Deep Reinforcement Learning (DRL) method that directly integrates multiple objectives and real-time traffic constraints into the speed control policy.

Suggested Citation

  • Liu, Enze & Lin, Zhiyuan & Chen, Haibo & Jia, Dongyao & Liu, Ye & Guo, Junhua & Li, Tiezhu & Wei, Tangjian, 2025. "Multiobjective eco-driving speed optimisation with real-time traffic: Balancing fuel, NOx, and travel time," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225014355
    DOI: 10.1016/j.energy.2025.135793
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225014355
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135793?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    2. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    3. Liu, Yonggang & Wu, Yitao & Wang, Xiangyu & Li, Liang & Zhang, Yuanjian & Chen, Zheng, 2023. "Energy management for hybrid electric vehicles based on imitation reinforcement learning," Energy, Elsevier, vol. 263(PC).
    4. Li, Daofei & Jiang, Yangye & Shen, Yijie, 2024. "Intersection eco-driving for automated vehicles: SMPC-based strategies for handling leading vehicle starting-up uncertainties," Energy, Elsevier, vol. 302(C).
    5. Cui, Yuepeng & Xu, Hao & Zou, Fumin & Chen, Zhihui & Gong, Kuangmin, 2021. "Optimization based method to develop representative driving cycle for real-world fuel consumption estimation," Energy, Elsevier, vol. 235(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. Carolina Osorio & Kanchana Nanduri, 2015. "Energy-Efficient Urban Traffic Management: A Microscopic Simulation-Based Approach," Transportation Science, INFORMS, vol. 49(3), pages 637-651, August.
    8. Wang, Yongfeng & Li, Shuguang & Bu sinnah, Zainab Ali & Ghandour, Raymond & Khan, Mohammad Nadeem & Ali, H. Elhosiny, 2024. "Optimizing energy efficiency and emission reduction: Leveraging the power of machine learning in an integrated compressed air energy storage-solid oxide fuel cell system," Energy, Elsevier, vol. 313(C).
    9. Pablo Fernández-Yáñez & José A. Soriano & Carmen Mata & Octavio Armas & Benjamín Pla & Vicente Bermúdez, 2021. "Simulation of Optimal Driving for Minimization of Fuel Consumption or NOx Emissions in a Diesel Vehicle," Energies, MDPI, vol. 14(17), pages 1-15, September.
    10. Matthias Ehrgott, 2005. "Multicriteria Optimization," Springer Books, Springer, edition 0, number 978-3-540-27659-3, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Iqbal, Najam & He, Guanzhang & Wang, Hu & Lin, Zhiqiang & Zheng, Zunqing & Yao, Mingfa, 2025. "Holistic energy management strategy for hybrid electric heavy-duty vehicles based on proximal policy optimization with the consideration of cabin temperature comfort," Energy, Elsevier, vol. 326(C).
    2. Jia, Chunchun & Li, Kunang & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao, 2023. "Health-aware energy management strategy for fuel cell hybrid bus considering air-conditioning control based on TD3 algorithm," Energy, Elsevier, vol. 283(C).
    3. Chen, Fujun & Wang, Bowen & Ni, Meng & Gong, Zhichao & Jiao, Kui, 2024. "Online energy management strategy for ammonia-hydrogen hybrid electric vehicles harnessing deep reinforcement learning," Energy, Elsevier, vol. 301(C).
    4. Kunang Li & Chunchun Jia & Xuefeng Han & Hongwen He, 2023. "A Novel Minimal-Cost Power Allocation Strategy for Fuel Cell Hybrid Buses Based on Deep Reinforcement Learning Algorithms," Sustainability, MDPI, vol. 15(10), pages 1-15, May.
    5. Jia, Chunchun & Liu, Wei & He, Hongwen & Chau, K.T., 2025. "Health-conscious energy management for fuel cell vehicles: An integrated thermal management strategy for cabin and energy source systems," Energy, Elsevier, vol. 333(C).
    6. Nie, Zhigen & Song, Hao & Lian, Yufeng & Shi, Zhuangfeng, 2025. "BiLSTM-ATTENTION-CNN predictive energy management based on ISSA optimization for intelligent fuel cell hybrid vehicle platoon," Energy, Elsevier, vol. 324(C).
    7. T. D. Chuong & V. Jeyakumar, 2025. "Adjustable robust multiobjective linear optimization: Pareto optimal solutions via conic programming," Annals of Operations Research, Springer, vol. 346(2), pages 895-916, March.
    8. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
    9. Yichen Lu & Chao Yang & Jun Yang, 2022. "A multi-objective humanitarian pickup and delivery vehicle routing problem with drones," Annals of Operations Research, Springer, vol. 319(1), pages 291-353, December.
    10. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    11. Wu, Weitiao & Lin, Yue & Liu, Ronghui & Jin, Wenzhou, 2022. "The multi-depot electric vehicle scheduling problem with power grid characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 322-347.
    12. Bogdana Stanojević & Milan Stanojević & Sorin Nădăban, 2021. "Reinstatement of the Extension Principle in Approaching Mathematical Programming with Fuzzy Numbers," Mathematics, MDPI, vol. 9(11), pages 1-16, June.
    13. Stelios Rozakis & Athanasios Kampas, 2022. "An interactive multi-criteria approach to admit new members in international environmental agreements," Operational Research, Springer, vol. 22(4), pages 3461-3487, September.
    14. Chambers, Robert G., 2024. "Numeraire choice, shadow profit, and inefficiency measurement," European Journal of Operational Research, Elsevier, vol. 319(2), pages 658-668.
    15. dos Santos, Daniela Scherer & Klamroth, Kathrin & Martins, Pedro & Paquete, Luís, 2025. "Solving the Multiobjective Quasi-clique Problem," European Journal of Operational Research, Elsevier, vol. 323(2), pages 409-424.
    16. Xingxing Wang & Jiaying Ji & Junyi Li & Zhou Zhao & Hongjun Ni & Yu Zhu, 2025. "Review and Outlook of Fuel Cell Power Systems for Commercial Vehicles, Buses, and Heavy Trucks," Sustainability, MDPI, vol. 17(13), pages 1-30, July.
    17. Manuel V. C. Vieira & Margarida Carvalho, 2023. "Lexicographic optimization for the multi-container loading problem with open dimensions for a shoe manufacturer," 4OR, Springer, vol. 21(3), pages 491-512, September.
    18. Lan, Penghang & Chen, She & Li, Qihang & Li, Kelin & Wang, Feng & Zhao, Yaoxun, 2024. "Intelligent hydrogen-ammonia combined energy storage system with deep reinforcement learning," Renewable Energy, Elsevier, vol. 237(PB).
    19. Fatemeh Ghandi & Mostafa Davtalab-Olyaie & Masoud Asgharian, 2024. "Peer-evaluation in centrally managed systems," Annals of Operations Research, Springer, vol. 333(1), pages 439-459, February.
    20. Petr Iakovlevitch Ekel & Matheus Pereira Libório & Laura Cozzi Ribeiro & Mateus Alberto Dorna de Oliveira Ferreira & Joel Gomes Pereira Junior, 2024. "Multi-Criteria Decision under Uncertainty as Applied to Resource Allocation and Its Computing Implementation," Mathematics, MDPI, vol. 12(6), pages 1-20, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225014355. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.