IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i11p1775-d117489.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/11/1775/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/11/1775/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Leslie C. Edie, 1961. "Car-Following and Steady-State Theory for Noncongested Traffic," Operations Research, INFORMS, vol. 9(1), pages 66-76, February.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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.
    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. Piotr Wróblewski & Jerzy Kupiec & Wojciech Drożdż & Wojciech Lewicki & Jarosław Jaworski, 2021. "The Economic Aspect of Using Different Plug-In Hybrid Driving Techniques in Urban Conditions," Energies, MDPI, vol. 14(12), pages 1-17, June.
    2. Chaoying Xia & Zhiming DU & Cong Zhang, 2017. "A Single-Degree-of-Freedom Energy Optimization Strategy for Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-23, July.
    3. 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).
    4. Zeyu Chen & Jiahuan Lu & Bo Liu & Nan Zhou & Shijie Li, 2020. "Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries," Energies, MDPI, vol. 13(10), pages 1-15, May.
    5. 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.
    6. Wang, Yue & Zeng, Xiaohua & Song, Dafeng & Yang, Nannan, 2019. "Optimal rule design methodology for energy management strategy of a power-split hybrid electric bus," Energy, Elsevier, vol. 185(C), pages 1086-1099.
    7. López-Ibarra, Jon Ander & Gaztañaga, Haizea & Saez-de-Ibarra, Andoni & Camblong, Haritza, 2020. "Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging," Applied Energy, Elsevier, vol. 280(C).
    8. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
    9. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    10. 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).
    11. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    12. Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
    13. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(C).
    14. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    15. Wu, Yue & Huang, Zhiwu & Liao, Hongtao & Chen, Bin & Zhang, Xiaoyong & Zhou, Yanhui & Liu, Yongjie & Li, Heng & Peng, Jun, 2020. "Adaptive power allocation using artificial potential field with compensator for hybrid energy storage systems in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    16. Zhangyu Lu & Xizheng Zhang, 2022. "Composite Non-Linear Control of Hybrid Energy-Storage System in Electric Vehicle," Energies, MDPI, vol. 15(4), pages 1-15, February.
    17. Liu, Hui & Li, Xunming & Wang, Weida & Han, Lijin & Xiang, Changle, 2018. "Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 427-444.
    18. Yang, Jibin & Xu, Xiaohui & Peng, Yiqiang & Deng, Pengyi & Wu, Xiaohua & Zhang, Jiye, 2022. "Hierarchical energy management of a hybrid propulsion system considering speed profile optimization," Energy, Elsevier, vol. 244(PB).
    19. Kegang Zhao & Jinghao Bei & Yanwei Liu & Zhihao Liang, 2019. "Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method," Energies, MDPI, vol. 12(17), pages 1-23, August.
    20. Zhang, Zhendong & He, Hongwen & Guo, Jinquan & Han, Ruoyan, 2020. "Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 280(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:jeners:v:10:y:2017:i:11:p:1775-:d:117489. 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.