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Minimizing Energy Consumption and Powertrain Cost of Fuel Cell Hybrid Vehicles with Consideration of Different Driving Cycles and SOC Ranges

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  • Yang Gao

    (School of Electromechanic Engineering, North Minzu University, Yinchuan 750021, China
    School of Electrical and Information Engineering, North Minzu University, Yinchuan 750021, China)

  • Changhong Liu

    (Mechanical Engineering Department, University of Kansas, Lawrence, KS 66045, USA)

  • Yuan Liang

    (School of Electromechanic Engineering, North Minzu University, Yinchuan 750021, China)

  • Sadegh Kouhestani Hamed

    (Mechanical Engineering Department, University of Kansas, Lawrence, KS 66045, USA)

  • Fuwei Wang

    (School of Electromechanic Engineering, North Minzu University, Yinchuan 750021, China)

  • Bo Bi

    (School of Electrical and Information Engineering, North Minzu University, Yinchuan 750021, China)

Abstract

Hydrogen consumption is an important performance indicator of fuel cell hybrid vehicles (FCHVs). Previous studies have investigated fuel consumption minimization both under different driving cycles and using various power management strategies. However, different constrains on battery state of charge (SOC) ranges can also affect fuel consumption dramatically. In this study, we develop a power-source sizing model based on the Pontryagin’s Minimum Principle (PMP) to minimize the fuel consumption of FCHVs, considering different driving cycles (i.e., FTP-72 and US06) and SOC ranges (conservative 50–60% and aggressive 20–80%). The different driving cycles and SOC ranges present the real-world circumstances of driving FCHVs to some extent. Fuel consumptions are compared both under different driving cycles and using different SOC ranges. The simulation results show an effective power size map, with outlines of an ineffective sizing zone and an inefficient sizing zone based on vehicle performance requirements (e.g., maximum speed and acceleration) and fuel consumption, respectively. Based on the developed model, an optimal power-source size map can be determined while minimizing both fuel consumption and powertrain cost as well as considering different driving cycles and SOC ranges.

Suggested Citation

  • Yang Gao & Changhong Liu & Yuan Liang & Sadegh Kouhestani Hamed & Fuwei Wang & Bo Bi, 2022. "Minimizing Energy Consumption and Powertrain Cost of Fuel Cell Hybrid Vehicles with Consideration of Different Driving Cycles and SOC Ranges," Energies, MDPI, vol. 15(17), pages 1-12, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6167-:d:897245
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    References listed on IDEAS

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    1. Jure Soldo & Branimir Škugor & Joško Deur, 2019. "Synthesis of Optimal Battery State-of-Charge Trajectory for Blended Regime of Plug-in Hybrid Electric Vehicles in the Presence of Low-Emission Zones and Varying Road Grades," Energies, MDPI, vol. 12(22), pages 1-21, November.
    2. Hosseinpour, Javad & Chitsaz, Ata & Liu, Lin & Gao, Yang, 2020. "Simulation of eco-friendly and affordable energy production via solid oxide fuel cell integrated with biomass gasification plant using various gasification agents," Renewable Energy, Elsevier, vol. 145(C), pages 757-771.
    3. Feroldi, Diego & Carignano, Mauro, 2016. "Sizing for fuel cell/supercapacitor hybrid vehicles based on stochastic driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 645-658.
    4. Tang, Xiaolin & Zhou, Haitao & Wang, Feng & Wang, Weida & Lin, Xianke, 2022. "Longevity-conscious energy management strategy of fuel cell hybrid electric Vehicle Based on deep reinforcement learning," Energy, Elsevier, vol. 238(PA).
    5. Liu, Yonggang & Liu, Junjun & Zhang, Yuanjian & Wu, Yitao & Chen, Zheng & Ye, Ming, 2020. "Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization," Energy, Elsevier, vol. 207(C).
    6. Song, Ke & Wang, Xiaodi & Li, Feiqiang & Sorrentino, Marco & Zheng, Bailin, 2020. "Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability," Energy, Elsevier, vol. 205(C).
    7. Adriano Ceschia & Toufik Azib & Olivier Bethoux & Francisco Alves, 2020. "Optimal Sizing of Fuel Cell Hybrid Power Sources with Reliability Consideration," Energies, MDPI, vol. 13(13), pages 1-18, July.
    8. Tom Fletcher & Kambiz Ebrahimi, 2020. "The Effect of Fuel Cell and Battery Size on Efficiency and Cell Lifetime for an L7e Fuel Cell Hybrid Vehicle," Energies, MDPI, vol. 13(22), pages 1-18, November.
    9. Qiao Zhang & Gang Li, 2019. "A Game Theory Energy Management Strategy for a Fuel Cell/Battery Hybrid Energy Storage System," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, January.
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