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Hydrogen consumption and durability assessment of fuel cell vehicles in realistic driving

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  • Piras, M.
  • De Bellis, V.
  • Malfi, E.
  • Novella, R.
  • Lopez-Juarez, M.

Abstract

This study proposes a predictive equivalent consumption minimization strategy (P-ECMS) that utilizes velocity prediction and considers various dynamic constraints to mitigate fuel cell degradation assessed using a dedicated sub-model. The objective is to reduce fuel consumption in real-world conditions without prior knowledge of the driving mission. The P-ECMS incorporates a velocity prediction layer into the Energy Management System. Comparative evaluations with a conventional adaptive-ECMS (A-ECMS), a standard ECMS with a well-tuned constant equivalence factor, and a rule-based strategy (RBS) are conducted across two driving cycles and three fuel cell dynamic restrictions (|di/dt|max≤ 0.1, 0.01, and 0.001 A/cm2s). The proposed strategy achieves H2 consumption reductions ranging from 1.4% to 3.0% compared to A-ECMS, and fuel consumption reductions of up to 6.1% when compared to RBS. Increasing dynamic limitations lead to increased H2 consumption and durability by up to 200% for all tested strategies.

Suggested Citation

  • Piras, M. & De Bellis, V. & Malfi, E. & Novella, R. & Lopez-Juarez, M., 2024. "Hydrogen consumption and durability assessment of fuel cell vehicles in realistic driving," Applied Energy, Elsevier, vol. 358(C).
    • Handle: RePEc:eee:appene:v:358:y:2024:i:c:s0306261923019232
    DOI: 10.1016/j.apenergy.2023.122559
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    References listed on IDEAS

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    1. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    2. Lin, Xinyou & Wu, Jiayun & Wei, Yimin, 2021. "An ensemble learning velocity prediction-based energy management strategy for a plug-in hybrid electric vehicle considering driving pattern adaptive reference SOC," Energy, Elsevier, vol. 234(C).
    3. Zhang, Zhendong & He, Hongwen & Guo, Jinquan & Han, Ruoyan, 2020. "Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 280(C).
    4. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    5. Sara Luciani & Andrea Tonoli, 2022. "Control Strategy Assessment for Improving PEM Fuel Cell System Efficiency in Fuel Cell Hybrid Vehicles," Energies, MDPI, vol. 15(6), pages 1-17, March.
    6. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    7. Zeng, Tao & Zhang, Caizhi & Zhang, Yanyi & Deng, Chenghao & Hao, Dong & Zhu, Zhongwen & Ran, Hongxu & Cao, Dongpu, 2021. "Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle," Energy, Elsevier, vol. 227(C).
    8. Desantes, J.M. & Novella, R. & Pla, B. & Lopez-Juarez, M., 2022. "A modeling framework for predicting the effect of the operating conditions and component sizing on fuel cell degradation and performance for automotive applications," Applied Energy, Elsevier, vol. 317(C).
    9. Zhen Zhu & Lingxin Zeng & Long Chen & Rong Zou & Yingfeng Cai, 2022. "Fuzzy Adaptive Energy Management Strategy for a Hybrid Agricultural Tractor Equipped with HMCVT," Agriculture, MDPI, vol. 12(12), pages 1-21, November.
    10. Min, Dehao & Song, Zhen & Chen, Huicui & Wang, Tianxiang & Zhang, Tong, 2022. "Genetic algorithm optimized neural network based fuel cell hybrid electric vehicle energy management strategy under start-stop condition," Applied Energy, Elsevier, vol. 306(PB).
    11. Song, Ke & Ding, Yuhang & Hu, Xiao & Xu, Hongjie & Wang, Yimin & Cao, Jing, 2021. "Degradation adaptive energy management strategy using fuel cell state-of-health for fuel economy improvement of hybrid electric vehicle," Applied Energy, Elsevier, vol. 285(C).
    12. Peng, Fei & Zhao, Yuanzhe & Chen, Ting & Zhang, Xuexia & Chen, Weirong & Zhou, Donghua & Li, Qi, 2018. "Development of robust suboptimal real-time power sharing strategy for modern fuel cell based hybrid tramways considering operational uncertainties and performance degradation," Applied Energy, Elsevier, vol. 226(C), pages 503-521.
    13. Desantes, J.M. & Novella, R. & Pla, B. & Lopez-Juarez, M., 2021. "Impact of fuel cell range extender powertrain design on greenhouse gases and NOX emissions in automotive applications," Applied Energy, Elsevier, vol. 302(C).
    14. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Real-time energy management for fuel cell electric vehicle using speed prediction-based model predictive control considering performance degradation," Applied Energy, Elsevier, vol. 304(C).
    15. Zhou, Yang & Ravey, Alexandre & Péra, Marie-Cecile, 2020. "Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer," Applied Energy, Elsevier, vol. 258(C).
    16. Remzi Can Samsun & Michael Rex & Laurent Antoni & Detlef Stolten, 2022. "Deployment of Fuel Cell Vehicles and Hydrogen Refueling Station Infrastructure: A Global Overview and Perspectives," Energies, MDPI, vol. 15(14), pages 1-34, July.
    17. Molina, S. & Novella, R. & Pla, B. & Lopez-Juarez, M., 2021. "Optimization and sizing of a fuel cell range extender vehicle for passenger car applications in driving cycle conditions," Applied Energy, Elsevier, vol. 285(C).
    18. Sadam Hussain & Muhammad Umair Ali & Gwan-Soo Park & Sarvar Hussain Nengroo & Muhammad Adil Khan & Hee-Je Kim, 2019. "A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-24, December.
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    2. Lech J. Sitnik & Monika Andrych-Zalewska & Radostin Dimitrov & Veselin Mihaylov & Anna Mielińska, 2024. "Assessment of Energy Footprint of Pure Hydrogen-Supplied Vehicles in Real Conditions of Long-Term Operation," Energies, MDPI, vol. 17(14), pages 1-25, July.

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