A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 8 Issue 12
Dec.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Yang Zhao, Yanguang Cai and Qiwen Song, "Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview," IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1948-1955, Dec. 2021. doi: 10.1109/JAS.2017.7510889
Citation: Yang Zhao, Yanguang Cai and Qiwen Song, "Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview," IEEE/CAA J. Autom. Sinica, vol. 8, no. 12, pp. 1948-1955, Dec. 2021. doi: 10.1109/JAS.2017.7510889

Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

doi: 10.1109/JAS.2017.7510889
More Information
  • The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and real-time traffic data, in which information fusion model and traffic prediction model are used to improve the information accuracy. Then, dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management. The solution is the reference for PHEV energy management control along the route. To improve the system's ability of handling changing situation, the study further explores predictive control model in the real-time control of the energy. A simulation is performed to model PHEV under above energy control strategy with route preview. The results show that the average fuel consumption of PHEV along the previewed route with model predictive control (MPC) strategy can be reduced compared with optimal strategy and base control strategy.

     

  • loading
  • Recommended by Associate Editor Xiangyang Zhao.
  • [1]
    F. R. Salmasi, "Control strategies for hybrid electric vehicles: Evolution, classification, comparison, and future trends, " IEEE Trans. Veh. Technol., vol. 56, no. 5, pp. 2393-2404, Sep. 2007.
    [2]
    J. Froehlich and J. Krumm, "Route prediction from trip observations, " presented at the Society of Automotive Engineers (SAE) World Congr., Detroit, MI, USA, 2008.
    [3]
    R. Simmons, B. Browning, Y. L. Zhang, and V. Sadekar, "Learning to predict driver route and destination intent, " in Proc. IEEE Intelligent Transportation Systems Conf., Toronto, Ont, Canada, 2006, pp. 127-132.
    [4]
    Q. M. Gong, Y. Y. Li, and Z. R. Peng, "Trip-based optimal power management of plug-in hybrid electric vehicles, " IEEE Trans. Veh. Technol., vol. 57, no. 6, pp. 3393-3401, Nov. 2008.
    [5]
    S. Kermani, R. Trigui, S. Delprat, B. Jeanneret, and T. M. Guerra, "PHIL implementation of energy management optimization for a parallel HEV on a predefined route, " IEEE Trans. Veh. Technol., vol. 60, no. 3, pp. 782-792, Mar. 2011.
    [6]
    S. Delprat, J. Lauber, T. M. Guerra, and J. Rimaux, "Control of a parallel hybrid powertrain: Optimal control, " IEEE Trans. Veh. Technol., vol. 53, no. 3, pp. 872-881, May 2004.
    [7]
    S. Stockar, V. Marano, M. Canova, G. Rizzoni, and L. Guzzella, "Energy-optimal control of plug-in hybrid electric vehicles for real-world driving cycles, " IEEE Trans. Veh. Technol., vol. 60, no. 7, pp. 2949-2962, Sep. 2011.
    [8]
    C. Zhang and A. Vahidi, "Route preview in energy management of plug-in hybrid vehicles, " IEEE Trans. Control Syst. Technol., vol. 20, no. 2, pp. 546-553, Mar. 2012.
    [9]
    L. Johannesson, S. Pettersson, and B. Egardt, "Predictive energy management of a 4QT series-parallel hybrid electric bus, " Control Eng. Pract., vol. 17, no. 12, pp. 1440-1453, Dec. 2009.
    [10]
    C. Y. Chen, Y. Wang, L. Li, J. M. Hu, and Z. Zhang, "The retrieval of intra-day trend and its influence on traffic prediction, " Transp. Res. C Emerg. Technol., vol. 22, pp. 103-118, Jun. 2012.
    [11]
    J. B. Sheu, Y. S. Huang, and L. W. Lan, "A real-time recurrent learning on predicting short-term temporal traffic dynamics for sustainable management, " Int. J. Environ. Sustain. Dev., vol. 8, no. 3-4, pp. 330-350, Apr. 2009.
    [12]
    M. Koot, J. T. B. A. Kessels, B. de Jager, W. P. M. H. Heemels, P. P. J. van den Bosch, and M. Steinbuch, "Energy management strategies for vehicular electric power systems, " IEEE Trans. Veh. Technol., vol. 54, no. 3, pp. 771-782, May 2005.
    [13]
    C. Zhang, A. Vahidi, P. Pisu, X. P. Li, and K. Tennant, "Role of terrain preview in energy management of hybrid electric vehicles, " IEEE Trans. Veh. Technol., vol. 59, no. 3, pp. 1139-1147, Mar. 2010.
    [14]
    L. D. Baskar, B. De Schutter, and H. Hellendoorn, "Traffic management for automated highway systems using model-based predictive control, " IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 838-847, Jun. 2012.
    [15]
    S. Lin, B. De Schutter, Y. G. Xi, and H. Hellendoorn, "Fast model predictive control for urban road networks via MILP, " IEEE Trans. Intell. Transp. Syst., vol. 12, no. 3, pp. 846-856, Sep. 2011.
    [16]
    Z. Chen, C. Yang, and S. N. Fang, "A convolutional neural network-based driving cycle prediction method for plug-in hybrid electric vehicles with bus route, " IEEE Access, vol. 8, pp. 3255-3264, 2020.
    [17]
    W. Zhou, Y. Q. Chen, H. R. Zhai, and W. G. Zhang, "Predictive energy management for a plug-in hybrid electric vehicle using driving profile segmentation and energy-based analytical SoC planning", Energy, vol. 220, pp. 119700, Apr. 2021.
    [18]
    Y. T. Wu, Y. J. Zhang, G. Li, J. W. Shen, Z. Chen, and Y. G. Liu, "A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks", Energy, vol. 208, pp. 118366, Oct. 2020.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(1)

    Article Metrics

    Article views (715) PDF downloads(75) Cited by()

    Highlights

    • The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and realtime traffific data, in which information fusion model and traffific prediction model are used to improve the information accuracy.
    • This paper presents real-time control strategies for PHEV energy management that takes advantage of traffific information preview. The proposed strategies aim at fuel minimization for PHEV. The dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management.
    • We focus on how MPC minimizes the cost and improves vehicle energy control. The route features are identifified with information fusion model and prediction model for historical and realtime traffific data. The average fuel consumption of PHEV along the previewed route with model predictive control (MPC) strategy can be reduced compared with optimal strategy and base control strategy.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return