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An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation

Author

Listed:
  • Hongwei Liu

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

  • Chantong Wang

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

  • Xin Zhao

    (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)

Abstract

Energy management strategies based on instantaneous optimization have been widely used in hybrid/plug-in hybrid electric vehicles (HEV/PHEV) in order to improve fuel economy while guaranteeing vehicle performance. In this study, an adaptive-equivalent consumption minimum strategy (A-ECMS) based on target driving cycle (TDC) generation was proposed for an extended-range electric bus (E-REB) operating on fixed routes. Firstly, a Hamilton function and a co-state equation for E-REB were determined according to the Pontryagin Minimum Principle (PMP). Then a series of TDCs were generated using Markov chain, and the optimal solutions under different initial state of charges (SOCs) were obtained using the PMP algorithm, forming the optimal initial co-state map. Thirdly, an adaptive co-state function consisting of fixed and dynamic terms was designed. The co-state map was interpolated using the initial SOC data and the vehicle driving data obtained by an Intelligent Transport System, and thereby the initial co-state values were solved and used as the fixed term. A segmented SOC reference curve was put forward according to the optimal SOC changing curves under different initial SOCs solved by using PMP. The dynamic term was determined using a PI controlling method and by real-time adjusting the co-states to follow the reference curve. Finally with the generated TDCs, the control effect of A-ECMS was compared with PMP and Constant-ECMS, which was showed A-ECMS provided the final SOC closer to the pre-set value and fully used the power of the batteries. The oil consumption solutions were close to the PMP optimized results and thereby the oil depletion was reduced.

Suggested Citation

  • Hongwei Liu & Chantong Wang & Xin Zhao & Chong Guo, 2018. "An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation," Energies, MDPI, vol. 11(7), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1805-:d:157222
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    References listed on IDEAS

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    5. Cheng-Ta Chung & Chien-Hsun Wu & Yi-Hsuan Hung, 2018. "Effects of Electric Circulation on the Energy Efficiency of the Power Split e-CVT Hybrid Systems," Energies, MDPI, vol. 11(9), pages 1-15, September.
    6. Umberto Previti & Sebastian Brusca & Antonio Galvagno & Fabio Famoso, 2022. "Influence of Energy Management System Control Strategies on the Battery State of Health in Hybrid Electric Vehicles," Sustainability, MDPI, vol. 14(19), pages 1-20, September.
    7. Andrzej Łebkowski, 2019. "Studies of Energy Consumption by a City Bus Powered by a Hybrid Energy Storage System in Variable Road Conditions," Energies, MDPI, vol. 12(5), pages 1-39, March.
    8. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
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    10. Yang, Ye & Zhang, Youtong & Tian, Jingyi & Li, Tao, 2020. "Adaptive real-time optimal energy management strategy for extender range electric vehicle," Energy, Elsevier, vol. 197(C).
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    12. Paweł Krawczyk & Artur Kopczyński & Jakub Lasocki, 2022. "Modeling and Simulation of Extended-Range Electric Vehicle with Control Strategy to Assess Fuel Consumption and CO 2 Emission for the Expected Driving Range," Energies, MDPI, vol. 15(12), pages 1-41, June.

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