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Fuel cell hybrid range-extender vehicle sizing: Parametric power optimization

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  • Taghavifar, Hadi

Abstract

E-transportation is a next generation technology for internal combustion engine phase-out. In present study, three drivetrain cases are designed: internal combustion engine (ICE), engine-fuel cell coupled configuration, and ICE-catalyst modes. First, the ICE-FC configuration is analyzed and compared with the ICE driven powertrain. Then, the emission of ICE-catalyst mode is compared with that of ICE-FC configuration. The proposed driveline parameters have been examined to monitor the emission, fuel economy, and efficiency of the overall hybrid system. The results indicated that in the new European driving cycle (NEDC), the mileage in hybrid mode has been extended from 3.4 km to 11.0 km. The results also show that by shifting from ICE alone to ICE-FC mode, the fuel consumption (gasoline) decreased from 26.24 1/100 km to 5.38 1/100 km. In addition, the NOx, CO, and HC species have been dropped by 47.7%, 61.7%, and 26.7%, respectively. The engine displacement, cell number of FC and GDL thickness are changed in two levels. Accordingly, when the GDL is thinner, the H2 consumption is marginally lower while the FC energy output increases up to 5400 kJ and the electric fuel economy is promoted by 6.43%.

Suggested Citation

  • Taghavifar, Hadi, 2021. "Fuel cell hybrid range-extender vehicle sizing: Parametric power optimization," Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:energy:v:229:y:2021:i:c:s0360544221010343
    DOI: 10.1016/j.energy.2021.120786
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    References listed on IDEAS

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    1. López, I. & Ibarra, E. & Matallana, A. & Andreu, J. & Kortabarria, I., 2019. "Next generation electric drives for HEV/EV propulsion systems: Technology, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Tang, Xiaolin & Lang, Kun & Xin, Zongke & Brighton, James, 2019. "Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge," Energy, Elsevier, vol. 173(C), pages 667-678.
    3. Hu, Xiaosong & Zhang, Xiaoqian & Tang, Xiaolin & Lin, Xianke, 2020. "Model predictive control of hybrid electric vehicles for fuel economy, emission reductions, and inter-vehicle safety in car-following scenarios," Energy, Elsevier, vol. 196(C).
    4. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    5. Xie, Shaobo & Hu, Xiaosong & Liu, Teng & Qi, Shanwei & Lang, Kun & Li, Huiling, 2019. "Predictive vehicle-following power management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 166(C), pages 701-714.
    6. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei & Guo, Qiuyi, 2018. "Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction," Energy, Elsevier, vol. 155(C), pages 838-852.
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    Cited by:

    1. Kim, Dong-Min & Lee, Soo-Gyung & Kim, Dae-Kee & Park, Min-Ro & Lim, Myung-Seop, 2022. "Sizing and optimization process of hybrid electric propulsion system for heavy-duty vehicle based on Gaussian process modeling considering traction motor characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Qian Zhang & Shaopeng Tian, 2023. "Energy Consumption Prediction and Control Algorithm for Hybrid Electric Vehicles Based on an Equivalent Minimum Fuel Consumption Model," Sustainability, MDPI, vol. 15(12), pages 1-17, June.

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    More about this item

    Keywords

    Catalyst; Fuel cell; Gasoline engine; H2 and fuel consumption; Hybrid vehicle;
    All these keywords.

    JEL classification:

    • H2 - Public Economics - - Taxation, Subsidies, and Revenue

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