IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v166y2019icp701-714.html
   My bibliography  Save this article

Predictive vehicle-following power management for plug-in hybrid electric vehicles

Author

Listed:
  • Xie, Shaobo
  • Hu, Xiaosong
  • Liu, Teng
  • Qi, Shanwei
  • Lang, Kun
  • Li, Huiling

Abstract

This paper presents a new integrated model predictive control (IMPC) method that combines power management and adaptive velocity control during vehicle-following scenarios in reality, for a plug-in hybrid electric vehicle (PHEV). Innovatively, the IMPC is able to plan the battery state of charge (SOC) and vehicular velocity trajectories, in order to improve fue economy and driving safety. To assess the performance of the IMPC, a comparison is performed with common charge-depleting and charge-sustaining (CDCS) and DP-based energy management strategies, where an improved full velocity difference model (IFVDM) is incorporated to simulate vehicle-following behavior. These solutions are examined using a real-world driving cycle. The results reveal an enormous potential of flexibly tuning the inter-vehicle distance to increase fuel economy. This is distinct from the rigid vehicle-following behavior of the IFVDM just for driving safety. Moreover, the proposed IMPC can ensure the battery charge depletion at the end of the trip. The quantitative results witness that the total cost of the IMPC with a preview horizon of 3s can be reduced by 17.9% and 36.1% for a 70 km city-bus route, compared to IFVDM-based DP and CDCS counterparts, respectively. In addition, the effect of the preview-horizon length on both fuel economy and computational time is examined.

Suggested Citation

  • Xie, Shaobo & Hu, Xiaosong & Liu, Teng & Qi, Shanwei & Lang, Kun & Li, Huiling, 2019. "Predictive vehicle-following power management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 166(C), pages 701-714.
    • Handle: RePEc:eee:energy:v:166:y:2019:i:c:p:701-714
    DOI: 10.1016/j.energy.2018.10.129
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421832125X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.10.129?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. Tian, He & Li, Shengbo Eben & Wang, Xu & Huang, Yong & Tian, Guangyu, 2018. "Data-driven hierarchical control for online energy management of plug-in hybrid electric city bus," Energy, Elsevier, vol. 142(C), pages 55-67.
    2. Kaijiang Yu & Junqi Yang, 2014. "Performance of a Nonlinear Real-Time Optimal Control System for HEVs/PHEVs during Car Following," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-14, June.
    3. Shaobo Xie & Huiling Li & Zongke Xin & Tong Liu & Lang Wei, 2017. "A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route," Energies, MDPI, vol. 10(9), pages 1-22, September.
    4. Li, Liang & You, Sixiong & Yang, Chao & Yan, Bingjie & Song, Jian & Chen, Zheng, 2016. "Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 162(C), pages 868-879.
    5. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
    6. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    7. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    8. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    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. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    2. Ghiasi, Mohammad, 2019. "Detailed study, multi-objective optimization, and design of an AC-DC smart microgrid with hybrid renewable energy resources," Energy, Elsevier, vol. 169(C), pages 496-507.
    3. 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).
    4. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    5. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    6. Hu, Maomao & Xiao, Fu & Jørgensen, John Bagterp & Wang, Shengwei, 2019. "Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 92-106.
    7. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    8. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    9. Ahmadian, Ali & Sedghi, Mahdi & Fgaier, Hedia & Mohammadi-ivatloo, Behnam & Golkar, Masoud Aliakbar & Elkamel, Ali, 2019. "PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect," Energy, Elsevier, vol. 175(C), pages 265-277.
    10. Mansour-lakouraj, Mohammad & Shahabi, Majid, 2019. "Comprehensive analysis of risk-based energy management for dependent micro-grid under normal and emergency operations," Energy, Elsevier, vol. 171(C), pages 928-943.
    11. Zhang, J.J. & Qu, Z.G. & Maharjan, A., 2019. "Numerical investigation of coupled optical-electrical-thermal processes for plasmonic solar cells at various angles of incident irradiance," Energy, Elsevier, vol. 174(C), pages 110-121.
    12. Li, Guiqiang & Shittu, Samson & Ma, Xiaoli & Zhao, Xudong, 2019. "Comparative analysis of thermoelectric elements optimum geometry between photovoltaic-thermoelectric and solar thermoelectric," Energy, Elsevier, vol. 171(C), pages 599-610.
    13. Zhang, Xian & Chan, K.W. & Wang, Huaizhi & Hu, Jiefeng & Zhou, Bin & Zhang, Yan & Qiu, Jing, 2019. "Game-theoretic planning for integrated energy system with independent participants considering ancillary services of power-to-gas stations," Energy, Elsevier, vol. 176(C), pages 249-264.
    14. 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.
    15. Talaat, M. & Farahat, M.A. & Elkholy, M.H., 2019. "Renewable power integration: Experimental and simulation study to investigate the ability of integrating wave, solar and wind energies," Energy, Elsevier, vol. 170(C), pages 668-682.
    16. Zima, Wiesław, 2019. "Simulation of steam superheater operation under conditions of pressure decrease," Energy, Elsevier, vol. 172(C), pages 932-944.
    17. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    18. van Zyl-Bulitta, Verena Helen & Ritzel, Christian & Stafford, William & Wong, James Gien, 2019. "A compass to guide through the myriad of sustainable energy transition options across the global North-South divide," Energy, Elsevier, vol. 181(C), pages 307-320.
    19. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    20. 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.

    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:166:y:2019:i:c:p:701-714. 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.