IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i11p1796-d118059.html
   My bibliography  Save this article

Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

Author

Listed:
  • Hongwen He

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Jinquan Guo

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Nana Zhou

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Chao Sun

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Jiankun Peng

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

Abstract

This paper presents a freeway driving cycle (FDC) construction method based on traffic information. A float car collected different type of roads in California and we built a velocity fragment database. We selected a real freeway driving cycle (RFDC) and established the corresponding time traffic information tensor model by using the data in California Department of Transportation performance measure system (PeMS). The correlation of road velocity in the time dimension and spatial dimension are analyzed. According to the average velocity of road sections at different times, the kinematic fragments are stochastically selected in the velocity fragment database to construct a real-time FDC of each section. The comparison between construction freeway driving cycle (CFDC) and real freeway driving cycle (RFDC) show that the CFDC well reflects the RFDC characteristic parameters. Compared to its application in plug-in electric hybrid vehicle (PHEV) optimal energy management based on a dynamic programming (DP) algorithm, CFDC and RFDC fuel consumption are similar within approximately 5.09% error, and non-rush hour fuel economy is better than rush hour 3.51 (L/100 km) at non-rush hour, 4.29 (L/km) at rush hour)). Moreover, the fuel consumption ratio can be up to 13.17% in the same CFDC at non-rush hour.

Suggested Citation

  • Hongwen He & Jinquan Guo & Nana Zhou & Chao Sun & Jiankun Peng, 2017. "Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 10(11), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1796-:d:118059
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/11/1796/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/11/1796/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Chao, 2003. "Freeway Performance Measurement System (PeMS)," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6j93p90t, Institute of Transportation Studies, UC Berkeley.
    2. Chen, Zeyu & Xiong, Rui & Wang, Chun & Cao, Jiayi, 2017. "An on-line predictive energy management strategy for plug-in hybrid electric vehicles to counter the uncertain prediction of the driving cycle," Applied Energy, Elsevier, vol. 185(P2), pages 1663-1672.
    3. Hanho Son & Hyunsoo Kim, 2016. "Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses," Energies, MDPI, vol. 9(6), pages 1-18, May.
    4. Armas, Octavio & García-Contreras, Reyes & Ramos, Ángel, 2013. "Impact of alternative fuels on performance and pollutant emissions of a light duty engine tested under the new European driving cycle," Applied Energy, Elsevier, vol. 107(C), pages 183-190.
    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. Antonio Galvagno & Umberto Previti & Fabio Famoso & Sebastian Brusca, 2021. "An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles," Energies, MDPI, vol. 14(6), pages 1-16, March.
    2. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.
    3. Samuel Rodman Oprešnik & Tine Seljak & Rok Vihar & Marko Gerbec & Tomaž Katrašnik, 2018. "Real-World Fuel Consumption, Fuel Cost and Exhaust Emissions of Different Bus Powertrain Technologies," Energies, MDPI, vol. 11(8), pages 1-20, August.
    4. Hongwen, He & Jinquan, Guo & Jiankun, Peng & Huachun, Tan & Chao, Sun, 2018. "Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 95-107.

    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. Hongwen, He & Jinquan, Guo & Jiankun, Peng & Huachun, Tan & Chao, Sun, 2018. "Real-time global driving cycle construction and the application to economy driving pro system in plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 95-107.
    2. Li, Tianyu & Liu, Huiying & Wang, Hui & Yao, Yongming, 2020. "Hierarchical predictive control-based economic energy management for fuel cell hybrid construction vehicles," Energy, Elsevier, vol. 198(C).
    3. Ho, Sze-Hwee & Wong, Yiik-Diew & Chang, Victor Wei-Chung, 2014. "Evaluating the potential of biodiesel (via recycled cooking oil) use in Singapore, an urban city," Resources, Conservation & Recycling, Elsevier, vol. 91(C), pages 117-124.
    4. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    5. Tolgahan Kaya & Osman Akın Kutlar & Ozgur Oguz Taskiran, 2018. "Evaluation of the Effects of Biodiesel on Emissions and Performance by Comparing the Results of the New European Drive Cycle and Worldwide Harmonized Light Vehicles Test Cycle," Energies, MDPI, vol. 11(10), pages 1-14, October.
    6. Jing Lian & Shuang Liu & Linhui Li & Xuanzuo Liu & Yafu Zhou & Fan Yang & Lushan Yuan, 2017. "A Mixed Logical Dynamical-Model Predictive Control (MLD-MPC) Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles (PHEVs)," Energies, MDPI, vol. 10(1), pages 1-18, January.
    7. Horn, Michael & MacLeod, Jennifer & Liu, Meinan & Webb, Jeremy & Motta, Nunzio, 2019. "Supercapacitors: A new source of power for electric cars?," Economic Analysis and Policy, Elsevier, vol. 61(C), pages 93-103.
    8. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    9. Pei, Huanxin & Hu, Xiaosong & Yang, Yalian & Tang, Xiaolin & Hou, Cong & Cao, Dongpu, 2018. "Configuration optimization for improving fuel efficiency of power split hybrid powertrains with a single planetary gear," Applied Energy, Elsevier, vol. 214(C), pages 103-116.
    10. Wei, Changyin & Sun, Xiuxiu & Chen, Yong & Zang, Libin & Bai, Shujie, 2021. "Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle," Energy, Elsevier, vol. 230(C).
    11. Li, Ji & Zhou, Quan & He, Yinglong & Shuai, Bin & Li, Ziyang & Williams, Huw & Xu, Hongming, 2019. "Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    12. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
    13. Peng, Fei & Zhao, Yuanzhe & Chen, Ting & Zhang, Xuexia & Chen, Weirong & Zhou, Donghua & Li, Qi, 2018. "Development of robust suboptimal real-time power sharing strategy for modern fuel cell based hybrid tramways considering operational uncertainties and performance degradation," Applied Energy, Elsevier, vol. 226(C), pages 503-521.
    14. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Kong, Xiaodan & Yan, Xingda, 2021. "Optimal sizing and sensitivity analysis of a battery-supercapacitor energy storage system for electric vehicles," Energy, Elsevier, vol. 221(C).
    15. Sexton, Steven E., 2010. "Rationing Public Goods by Cooperation or Pecuniary Incentives: Evidence from the Spare-the-Air Program," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt5xs9r6t8, Department of Agricultural & Resource Economics, UC Berkeley.
    16. Herreros, J.M. & Jones, A. & Sukjit, E. & Tsolakis, A., 2014. "Blending lignin-derived oxygenate in enhanced multi-component diesel fuel for improved emissions," Applied Energy, Elsevier, vol. 116(C), pages 58-65.
    17. Luján, José Manuel & Serrano, José Ramón & Piqueras, Pedro & García-Afonso, Óscar, 2015. "Experimental assessment of a pre-turbo aftertreatment configuration in a single stage turbocharged diesel engine. Part 2: Transient operation," Energy, Elsevier, vol. 80(C), pages 614-627.
    18. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng & Li, Xiaoyu & Qu, Changhui, 2019. "Driving cycles construction for electric vehicles considering road environment: A case study in Beijing," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    19. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2023. "Investigation of novel intelligent energy management strategies for connected HEB considering global planning of fixed-route information," Energy, Elsevier, vol. 263(PB).
    20. Soto, Felipe & Marques, Gian & Torres-Jiménez, E. & Vieira, Bráulio & Lacerda, André & Armas, Octavio & Guerrero-Villar, F., 2019. "A comparative study of performance and regulated emissions in a medium-duty diesel engine fueled with sugarcane diesel-farnesane and sugarcane biodiesel-LS9," Energy, Elsevier, vol. 176(C), pages 392-409.

    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:jeners:v:10:y:2017:i:11:p:1796-:d:118059. 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.