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Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast

Author

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  • Weiyi Lin

    (School of Automobile and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
    National and Local Joint Engineering Research Center for Automotive Technology and Equipment, Hefei University of Technology, Hefei 230009, China)

  • Han Zhao

    (National and Local Joint Engineering Research Center for Automotive Technology and Equipment, Hefei University of Technology, Hefei 230009, China
    School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China)

  • Bingzhan Zhang

    (School of Automobile and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
    National and Local Joint Engineering Research Center for Automotive Technology and Equipment, Hefei University of Technology, Hefei 230009, China)

  • Ye Wang

    (Intelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, China)

  • Yan Xiao

    (Intelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, China)

  • Kang Xu

    (Intelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, China)

  • Rui Zhao

    (Intelligent Vehicle Division, Hozon New Energy Automobile Co., Ltd., Tongxiang 314505, China)

Abstract

Range-extended Electric Vehicles (REVs) have become popular due to their lack of emissions while driving in urban areas, and the elimination of range anxiety when traveling long distances with a combustion engine as the power source. The fuel consumption performance of REVs depends greatly on the energy management strategy (EMS). This article proposes a practical energy management solution for REVs based on an Adaptive Equivalent Fuel Consumption Minimization Strategy (A-ECMS), wherein the equivalent factor is dynamically optimized by the battery’s State of Charge (SoC) and traffic information provided by Intelligent Transportation Systems (ITS). Furthermore, a penalty function is incorporated with the A-ECMS strategy to achieve the quasi-optimal start–stop control of the range extender. The penalty function is designed based on more precise vehicle velocity forecasting through a nonlinear autoregressive network with exogeneous input (NARX). A model of the studied REV is established in the AVL Cruise environment and the proposed energy management strategy is set up in Matlab/Simulink. Lastly, the performance of the proposed strategy is evaluated over multiple Worldwide Light-duty Test Cycles (WLTC) and real-world driving cycles through model simulation. The simulation conditions are preset such that the range extender must be switched on to finish the planned route. Compared with the basic Charge-Depleting and Charge-Sustaining (CD-CS) strategy, the proposed A-ECMS strategy achieves a fuel-consumption benefit of up to 9%. With the implementation of range extender start–stop optimization, which is based on velocity forecasting, the fuel saving rate can be further improved by 6.7% to 18.2% compared to the base A-ECMS. The proposed strategy is energy efficient, with a simple structure, and it is intended to be implemented on the studied vehicle, which will be available on the market at the end of October 2022.

Suggested Citation

  • Weiyi Lin & Han Zhao & Bingzhan Zhang & Ye Wang & Yan Xiao & Kang Xu & Rui Zhao, 2022. "Predictive Energy Management Strategy for Range-Extended Electric Vehicles Based on ITS Information and Start–Stop Optimization with Vehicle Velocity Forecast," Energies, MDPI, vol. 15(20), pages 1-27, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7774-:d:948584
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    References listed on IDEAS

    as
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