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Modelling and predicting energy consumption of a range extender fuel cell hybrid vehicle

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  • Zeng, Tao
  • Zhang, Caizhi
  • Hu, Minghui
  • Chen, Yan
  • Yuan, Changrong
  • Chen, Jingrui
  • Zhou, Anjian

Abstract

Energy consumption is an important economical index of a fuel cell hybrid vehicle (FCHV). To analyse the energy consumption of a range extender FCHV and reduce the cost of experiments, this study developed a nonlinear regression model of the powertrain of the vehicle to predict the current and voltage on the DC bus, which were used in the investigation of energy consumption, by using the intelligent algorithms including Back Propagation neural network (BP), Genetic Algorithm-Back Propagation neural network (GABP) and least square support vector machine (LSSVM). The model based on the LSSVM achieves the best predicted performance and can consider the nonlinear characteristics of the powertrain quite well. A case study was discussed by applying the obtained model and integrated with a hierarchical energy management strategy (HEMS). The specific results of energy consumption showed that it is feasible to use the predicted data of the obtained model in the analysis of the energy consumption of the FCHV.

Suggested Citation

  • Zeng, Tao & Zhang, Caizhi & Hu, Minghui & Chen, Yan & Yuan, Changrong & Chen, Jingrui & Zhou, Anjian, 2018. "Modelling and predicting energy consumption of a range extender fuel cell hybrid vehicle," Energy, Elsevier, vol. 165(PB), pages 187-197.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pb:p:187-197
    DOI: 10.1016/j.energy.2018.09.086
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    8. Dengfeng Zhao & Haiyang Li & Junjian Hou & Pengliang Gong & Yudong Zhong & Wenbin He & Zhijun Fu, 2023. "A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption," Energies, MDPI, vol. 16(14), pages 1-20, July.
    9. Lopez-Juarez, M. & Rockstroh, T. & Novella, R. & Vijayagopal, R., 2024. "A methodology to develop multi-physics dynamic fuel cell system models validated with vehicle realistic drive cycle data," Applied Energy, Elsevier, vol. 358(C).
    10. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    11. Zeng, Tao & Zhang, Caizhi & Hao, Dong & Cao, Dongpu & Chen, Jiawei & Chen, Jinrui & Li, Jin, 2020. "Data-driven approach for short-term power demand prediction of fuel cell hybrid vehicles," Energy, Elsevier, vol. 208(C).
    12. Ma, Shuai & Lin, Meng & Lin, Tzu-En & Lan, Tian & Liao, Xun & Maréchal, François & Van herle, Jan & Yang, Yongping & Dong, Changqing & Wang, Ligang, 2021. "Fuel cell-battery hybrid systems for mobility and off-grid applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    13. Sun, Lejia & Jia, Jingqi & Wang, QuanLi & Zhang, Yimeng, 2024. "A novel multiphase DC/DC boost converter for interaction of solar energy and hydrogen fuel cell in hybrid electric vehicles," Renewable Energy, Elsevier, vol. 229(C).
    14. Zou, Weitao & Li, Jianwei & Yang, Qingqing & Wan, Xinming & He, Yuntang & Lan, Hao, 2023. "A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle," Applied Energy, Elsevier, vol. 334(C).
    15. Zhou, Cheng & Chen, Xiyang, 2019. "Predicting energy consumption: A multiple decomposition-ensemble approach," Energy, Elsevier, vol. 189(C).
    16. Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
    17. Özçelep, Yasin & Sevgen, Selcuk & Samli, Ruya, 2020. "A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression," Renewable Energy, Elsevier, vol. 156(C), pages 570-578.
    18. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.

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