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An Economical Route Planning Method for Plug-In Hybrid Electric Vehicle in Real World

Author

Listed:
  • Yuanjian Zhang

    (State Key Laboratory of Automotive Dynamic Simulation and Control, Jilin University, Changchun 130022, China)

  • Liang Chu

    (State Key Laboratory of Automotive Dynamic Simulation and Control, Jilin University, Changchun 130022, China)

  • Zicheng Fu

    (State Key Laboratory of Automotive Dynamic Simulation and Control, Jilin University, Changchun 130022, China)

  • Nan Xu

    (State Key Laboratory of Automotive Dynamic Simulation and Control, Jilin University, Changchun 130022, China)

  • Chong Guo

    (State Key Laboratory of Automotive Dynamic Simulation and Control, Jilin University, Changchun 130022, China)

  • Yukuan Li

    (State Key Laboratory of Automotive Dynamic Simulation and Control, Jilin University, Changchun 130022, China)

  • Zhouhuan Chen

    (China Automotive Engineering Research Institute Co., Ltd, Chongqing 401122, China)

  • Hanwen Sun

    (China Automotive Engineering Research Institute Co., Ltd, Chongqing 401122, China)

  • Qin Bai

    (China Automotive Engineering Research Institute Co., Ltd, Chongqing 401122, China)

  • Yang Ou

    (China Automotive Engineering Research Institute Co., Ltd, Chongqing 401122, China)

Abstract

Relieving the adverse effects of automobiles on the environment and natural resources has drawn the attention of numerous researchers. This paper seeks a new path to reach a target by focusing on the synergy of the vehicle and the environment. A real-time economical route planning method for a plug-in hybrid electric vehicle (PHEV) is proposed. Three main contributions have been made. Firstly, a real comparison test is performed to provide rudimentary understanding of the difference in energy usage and route planning between PHEVs and conventional vehicles. Secondly, an approach to obtain PHEV customized data is developed for road weight calculation, which is the essential step in route planning. This method incorporates traffic data from conventional vehicles with the PHEV simulation model, obtaining the required data. Thirdly, the travel expense estimation model (TEEM) is designed. The TEEM could be applied to calculate the road weight of each road segment considering the impact on energy consumption with respect to environmental factors, providing the grounds for route planning. The proposed method to plan an economical route is evaluated, and the results justify its validation and ability to improve fuel economy.

Suggested Citation

  • Yuanjian Zhang & Liang Chu & Zicheng Fu & Nan Xu & Chong Guo & Yukuan Li & Zhouhuan Chen & Hanwen Sun & Qin Bai & Yang Ou, 2017. "An Economical Route Planning Method for Plug-In Hybrid Electric Vehicle in Real World," Energies, MDPI, vol. 10(11), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1775-:d:117489
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    References listed on IDEAS

    as
    1. Chen, Su-Mei & He, Ling-Yun, 2014. "Welfare loss of China's air pollution: How to make personal vehicle transportation policy," China Economic Review, Elsevier, vol. 31(C), pages 106-118.
    2. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    3. Shabbir, Wassif & Evangelou, Simos A., 2014. "Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency," Applied Energy, Elsevier, vol. 135(C), pages 512-522.
    4. Garth Heutel & Erich Muehlegger, 2015. "Consumer Learning and Hybrid Vehicle Adoption," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(1), pages 125-161, September.
    5. Leslie C. Edie, 1961. "Car-Following and Steady-State Theory for Noncongested Traffic," Operations Research, INFORMS, vol. 9(1), pages 66-76, February.
    6. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    7. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
    8. Koetse, Mark J. & Hoen, Anco, 2014. "Preferences for alternative fuel vehicles of company car drivers," Resource and Energy Economics, Elsevier, vol. 37(C), pages 279-301.
    9. Sajjad, H. & Masjuki, H.H. & Varman, M. & Kalam, M.A. & Arbab, M.I. & Imtenan, S. & Rahman, S.M. Ashrafur, 2014. "Engine combustion, performance and emission characteristics of gas to liquid (GTL) fuels and its blends with diesel and bio-diesel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 961-986.
    10. 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.
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