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Adaptive Mode Selection Strategy for Series-Parallel Hybrid Electric Vehicles Based on Variable Power Reserve

Author

Listed:
  • Jingzheng Fan

    (Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China
    School of Mechanical Engineering, Tianjin University, Tianjin 300072, China)

  • Bingfeng Zu

    (Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China
    School of Mechanical Engineering, Tianjin University, Tianjin 300072, China)

  • Jianwei Zhou

    (Tianjin Trumpjet Power Technology Co., Ltd., Tianjin 300072, China)

  • Zhen Wang

    (Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China
    School of Mechanical Engineering, Tianjin University, Tianjin 300072, China)

  • Haopeng Wang

    (Internal Combustion Engine Research Institute, Tianjin University, Tianjin 300072, China)

Abstract

When the series-parallel hybrid electric vehicle exits the pure electric mode, the battery provides power for the drive motor and integrated starter generator ( ISG ) to drive the vehicle and start the engine. If the battery discharge power is insufficient, the driving power will drop, which will inhibit the vehicle from accelerating and impair drivability. Considering that the mode selection strategy determines the timing of mode switching, this paper proposes an adaptive mode selection strategy based on variable power reserve to allow the vehicle to switch mode considering the battery power limitation. The effectiveness of this strategy is verified by simulation, and its influence on fuel consumption and battery utilization is analyzed. Compared with the mode selection strategy based on logic thresholds at the same initial battery state of charge ( SOC ), under the high-speed and aggressive US06 cycle, the total driving power drop is reduced by 74.2%, and the over-discharge power of the battery is fully restrained while keep almost the same fuel consumption; under the city FTP cycle, the total driving power drop is reduced 65%, and fuel consumption is reduced while maintaining SOC at a reasonable level.

Suggested Citation

  • Jingzheng Fan & Bingfeng Zu & Jianwei Zhou & Zhen Wang & Haopeng Wang, 2021. "Adaptive Mode Selection Strategy for Series-Parallel Hybrid Electric Vehicles Based on Variable Power Reserve," Energies, MDPI, vol. 14(11), pages 1-18, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3171-:d:564679
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    References listed on IDEAS

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    1. Esfandyari, M.J. & Esfahanian, V. & Hairi Yazdi, M.R. & Nehzati, H. & Shekoofa, O., 2019. "A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle," Energy, Elsevier, vol. 176(C), pages 505-520.
    2. Piotr Wróblewski & Wojciech Drożdż & Wojciech Lewicki & Paweł Miązek, 2021. "Methodology for Assessing the Impact of Aperiodic Phenomena on the Energy Balance of Propulsion Engines in Vehicle Electromobility Systems for Given Areas," Energies, MDPI, vol. 14(8), pages 1-24, April.
    3. Piotr Wróblewski & Wojciech Drożdż & Wojciech Lewicki & Jakub Dowejko, 2021. "Total Cost of Ownership and Its Potential Consequences for the Development of the Hydrogen Fuel Cell Powered Vehicle Market in Poland," Energies, MDPI, vol. 14(8), pages 1-25, April.
    4. Jingxian Hao & Zhuoping Yu & Zhiguo Zhao & Peihong Shen & Xiaowen Zhan, 2016. "Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm," Energies, MDPI, vol. 9(12), pages 1-24, November.
    5. Zheng, Fangdan & Jiang, Jiuchun & Sun, Bingxiang & Zhang, Weige & Pecht, Michael, 2016. "Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles," Energy, Elsevier, vol. 113(C), pages 64-75.
    6. Onat, Nuri Cihat & Kucukvar, Murat & Tatari, Omer, 2015. "Conventional, hybrid, plug-in hybrid or electric vehicles? State-based comparative carbon and energy footprint analysis in the United States," Applied Energy, Elsevier, vol. 150(C), pages 36-49.
    7. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).
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    1. Dapai Shi & Junjie Guo & Kangjie Liu & Qingling Cai & Zhenghong Wang & Xudong Qu, 2023. "Research on an Improved Rule-Based Energy Management Strategy Enlightened by the DP Optimization Results," Sustainability, MDPI, vol. 15(13), pages 1-13, July.

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