IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p9173-d615233.html
   My bibliography  Save this article

Role of Model Predictive Control for Enhancing Eco-Driving of Electric Vehicles in Urban Transport System of Japan

Author

Listed:
  • Zifei Nie

    (Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan)

  • Hooman Farzaneh

    (Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan)

Abstract

Electrification alters the energy demand and environmental impacts of vehicles, which brings about new challenges for sustainability in the transport sector. To further enhance the energy economy of electric vehicles (EVs) and offer an energy-efficient driving strategy for next-generation intelligent mobility in daily synthetic traffic situations with mixed driving scenarios, the model predictive control (MPC) algorithm is exploited to develop a predictive cruise control (PCC) system for eco-driving based on a detailed driving scenario switching logic (DSSL). The proposed PCC system is designed hierarchically into three typical driving scenarios, including car-following, signal anticipation, and free driving scenario, using one linear MPC and two nonlinear MPC controllers, respectively. The performances of the proposed tri-level MPC-based PCC system for EV eco-driving were investigated by a numerical simulation using the real road and traffic data of Japan under three typical driving scenarios and an integrated traffic situation. The results showed that the proposed PCC system can not only realize driving safety and comfortability, but also harvest considerable energy-saving rates during either car-following (16.70%), signal anticipation (12.50%), and free driving scenario (30.30%), or under the synthetic traffic situation (19.97%) in urban areas of Japan.

Suggested Citation

  • Zifei Nie & Hooman Farzaneh, 2021. "Role of Model Predictive Control for Enhancing Eco-Driving of Electric Vehicles in Urban Transport System of Japan," Sustainability, MDPI, vol. 13(16), pages 1-37, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9173-:d:615233
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/9173/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/16/9173/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hu, Xiaosong & Zhang, Xiaoqian & Tang, Xiaolin & Lin, Xianke, 2020. "Model predictive control of hybrid electric vehicles for fuel economy, emission reductions, and inter-vehicle safety in car-following scenarios," Energy, Elsevier, vol. 196(C).
    2. Yi, Chenyu & Epureanu, Bogdan I. & Hong, Sung-Kwon & Ge, Tony & Yang, Xiao Guang, 2016. "Modeling, control, and performance of a novel architecture of hybrid electric powertrain system," Applied Energy, Elsevier, vol. 178(C), pages 454-467.
    3. Yang, Chao & Wang, Muyao & Wang, Weida & Pu, Zesong & Ma, Mingyue, 2021. "An efficient vehicle-following predictive energy management strategy for PHEV based on improved sequential quadratic programming algorithm," Energy, Elsevier, vol. 219(C).
    4. Ma, Fangwu & Yang, Yu & Wang, Jiawei & Liu, Zhenze & Li, Jinhang & Nie, Jiahong & Shen, Yucheng & Wu, Liang, 2019. "Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication," Energy, Elsevier, vol. 189(C).
    5. Alshehry, Atef Saad & Belloumi, Mounir, 2017. "Study of the environmental Kuznets curve for transport carbon dioxide emissions in Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1339-1347.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Simin Hesami & Majid Vafaeipour & Cedric De Cauwer & Evy Rombaut & Lieselot Vanhaverbeke & Thierry Coosemans, 2023. "Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility," Energies, MDPI, vol. 16(18), pages 1-19, September.
    2. Zhang, Hanyu & Du, Lili, 2023. "Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part I: Modeling and solution algorithm design," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 174-198.

    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. Nie, Zifei & Farzaneh, Hooman, 2022. "Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-netw," Energy, Elsevier, vol. 241(C).
    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. 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).
    4. Ruan, Shumin & Ma, Yue & Yang, Ningkang & Yan, Qi & Xiang, Changle, 2023. "Multiobjective optimization of longitudinal dynamics and energy management for HEVs based on nash bargaining game," Energy, Elsevier, vol. 262(PA).
    5. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    6. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    7. Wang, Weida & Chen, Yincong & Yang, Chao & Li, Ying & Xu, Bin & Xiang, Changle, 2022. "An enhanced hypotrochoid spiral optimization algorithm based intertwined optimal sizing and control strategy of a hybrid electric air-ground vehicle," Energy, Elsevier, vol. 257(C).
    8. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    9. Taghavifar, Hadi, 2021. "Fuel cell hybrid range-extender vehicle sizing: Parametric power optimization," Energy, Elsevier, vol. 229(C).
    10. Xin-Wei Li & Hong-Zhi Miao, 2023. "How to Incorporate Autonomous Vehicles into the Carbon Neutrality Framework of China: Legal and Policy Perspectives," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    11. Danish, & Baloch, Muhammad Awais & Wang, Bo, 2019. "Analyzing the role of governance in CO2 emissions mitigation: The BRICS experience," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 119-125.
    12. Gao, Kai & Luo, Pan & Xie, Jin & Chen, Bin & Wu, Yue & Du, Ronghua, 2023. "Energy management of plug-in hybrid electric vehicles based on speed prediction fused driving intention and LIDAR," Energy, Elsevier, vol. 284(C).
    13. Nor Salwati Othman & Nurul Hezlin Mohamed Hariri, 2021. "Estimating the Causality and Elasticities of Residential Electricity Consumption for Malaysia," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 335-346.
    14. Li, Haijian & Zhang, Junjie & Sun, Xiaoliang & Niu, Jun & Zhao, Xiaohua, 2022. "A survey of vehicle group behaviors simulation under a connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    15. Qin, Yanyan & Liu, Mingxuan & Hao, Wei, 2024. "Energy-optimal car-following model for connected automated vehicles considering traffic flow stability," Energy, Elsevier, vol. 298(C).
    16. Pi, Dawei & Xue, Pengyu & Wang, Weihua & Xie, Boyuan & Wang, Hongliang & Wang, Xianhui & Yin, Guodong, 2023. "Automotive platoon energy-saving: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    17. Songlin Yang & Jingan Feng & Bao Song, 2021. "Research on Decoupled Optimal Control of Straight-Line Driving Stability of Electric Vehicles Driven by Four-Wheel Hub Motors," Energies, MDPI, vol. 14(18), pages 1-25, September.
    18. Li, Bin & Dong, Xujun & Wen, Jianghui, 2022. "Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour," Energy, Elsevier, vol. 243(C).
    19. Lv, Chengkun & Huang, Qian & Chang, Juntao & Wang, Ziao & Zheng, Jialin & Yu, Daren, 2023. "Mode transition path optimization for turbine-based combined-cycle ramjet stage under uncertainty propagation of integrated airframe-propulsion system," Energy, Elsevier, vol. 268(C).
    20. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).

    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:gam:jsusta:v:13:y:2021:i:16:p:9173-:d:615233. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.