IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v289y2024ics0360544223033108.html
   My bibliography  Save this article

A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes

Author

Listed:
  • Li, Bingbing
  • Zhuang, Weichao
  • Zhang, Hao
  • Zhao, Ruixuan
  • Liu, Haoji
  • Qu, Linghu
  • Zhang, Jianrun
  • Chen, Boli

Abstract

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies.

Suggested Citation

  • Li, Bingbing & Zhuang, Weichao & Zhang, Hao & Zhao, Ruixuan & Liu, Haoji & Qu, Linghu & Zhang, Jianrun & Chen, Boli, 2024. "A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes," Energy, Elsevier, vol. 289(C).
    • Handle: RePEc:eee:energy:v:289:y:2024:i:c:s0360544223033108
    DOI: 10.1016/j.energy.2023.129916
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223033108
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129916?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Hua, Min & Zhang, Cetengfei & Zhang, Fanggang & Li, Zhi & Yu, Xiaoli & Xu, Hongming & Zhou, Quan, 2023. "Energy management of multi-mode plug-in hybrid electric vehicle using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 348(C).
    2. Hu, Xiaosong & Zou, Yuan & Yang, Yalian, 2016. "Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization," Energy, Elsevier, vol. 111(C), pages 971-980.
    3. Barkenbus, Jack N., 2010. "Eco-driving: An overlooked climate change initiative," Energy Policy, Elsevier, vol. 38(2), pages 762-769, February.
    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. 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).
    2. Shaobo Xie & Xiaosong Hu & Kun Lang & Shanwei Qi & Tong Liu, 2018. "Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    3. Ahmed, Sumayyah & Sanguinetti, Angela, 2015. "OBDEnergy: Making Metrics Meaningful in Eco-driving Feedback," Institute of Transportation Studies, Working Paper Series qt0x73t2jw, Institute of Transportation Studies, UC Davis.
    4. Pietro Stabile & Federico Ballo & Giorgio Previati & Giampiero Mastinu & Massimiliano Gobbi, 2023. "Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios," Energies, MDPI, vol. 16(3), pages 1-19, January.
    5. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    6. Yuan, Weichang & Frey, H. Christopher, 2020. "Potential for metro rail energy savings and emissions reduction via eco-driving," Applied Energy, Elsevier, vol. 268(C).
    7. Wu, Xiaohua & Hu, Xiaosong & Yin, Xiaofeng & Zhang, Caiping & Qian, Shide, 2017. "Optimal battery sizing of smart home via convex programming," Energy, Elsevier, vol. 140(P1), pages 444-453.
    8. Xie, Shaobo & Lang, Kun & Qi, Shanwei, 2020. "Aerodynamic-aware coordinated control of following speed and power distribution for hybrid electric trucks," Energy, Elsevier, vol. 209(C).
    9. Nikoleta Mikušová & Gabriel Fedorko & Vieroslav Molnár & Martina Hlatká & Rudolf Kampf & Veronika Sirková, 2021. "Possibility of a Solution of the Sustainability of Transport and Mobility with the Application of Discrete Computer Simulation—A Case Study," Sustainability, MDPI, vol. 13(17), pages 1-24, September.
    10. Chattopadhyay, Kabitri & Kies, Alexander & Lorenz, Elke & von Bremen, Lüder & Heinemann, Detlev, 2017. "The impact of different PV module configurations on storage and additional balancing needs for a fully renewable European power system," Renewable Energy, Elsevier, vol. 113(C), pages 176-189.
    11. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.
    12. Hu, Dong & Huang, Chao & Yin, Guodong & Li, Yangmin & Huang, Yue & Huang, Hailong & Wu, Jingda & Li, Wenfei & Xie, Hui, 2024. "A transfer-based reinforcement learning collaborative energy management strategy for extended-range electric buses with cabin temperature comfort consideration," Energy, Elsevier, vol. 290(C).
    13. Strömberg, Helena & Karlsson, I.C. MariAnne & Rexfelt, Oskar, 2015. "Eco-driving: Drivers’ understanding of the concept and implications for future interventions," Transport Policy, Elsevier, vol. 39(C), pages 48-54.
    14. Pirouzi, Sasan & Aghaei, Jamshid & Niknam, Taher & Shafie-khah, Miadreza & Vahidinasab, Vahid & Catalão, João P.S., 2017. "Two alternative robust optimization models for flexible power management of electric vehicles in distribution networks," Energy, Elsevier, vol. 141(C), pages 635-651.
    15. Juliet Namukasa & Sheila Namagembe & Faridah Nakayima, 2020. "Fuel Efficiency Vehicle Adoption and Carbon Emissions in a Country Context," International Journal of Global Sustainability, Macrothink Institute, vol. 4(1), pages 1-21, December.
    16. Montag, Josef, 2015. "The simple economics of motor vehicle pollution: A case for fuel tax," Energy Policy, Elsevier, vol. 85(C), pages 138-149.
    17. Aurélien Saussay, 2019. "Dynamic heterogeneity: rational habits and the heterogeneity of household responses to gasoline prices," Post-Print hal-03632598, HAL.
    18. Carvalho, Irene & Baier, Thomas & Simoes, Ricardo & Silva, Arlindo, 2012. "Reducing fuel consumption through modular vehicle architectures," Applied Energy, Elsevier, vol. 93(C), pages 556-563.
    19. Echeverría, Lucía & Gimenez-Nadal, José Ignacio & Molina, José Alberto, 2021. "Carpooling: User Profiles and Well-being," IZA Discussion Papers 14736, Institute of Labor Economics (IZA).
    20. Grant-Muller, Susan & Usher, Mark, 2014. "Intelligent Transport Systems: The propensity for environmental and economic benefits," Technological Forecasting and Social Change, Elsevier, vol. 82(C), pages 149-166.

    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:289:y:2024:i:c:s0360544223033108. 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.