IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3364-d808861.html
   My bibliography  Save this article

Multi-Criteria Optimal Design for FUEL Cell Hybrid Power Sources

Author

Listed:
  • Adriano Ceschia

    (ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Sacley, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France)

  • Toufik Azib

    (ESTACA’LAB, S2ET Department, ESTACA Engineering School–Paris Sacley, 12 Avenue Paul Delouvrier, 78180 Montigny-le-Bretonneux, France)

  • Olivier Bethoux

    (GeePs, Group of Electrical Engineering—Paris, UMR CNRS 8507, CentraleSupélec School, University of Paris-Saclay, Sorbonne University, 3 Rue Joliot-Curie, 91192 Gif-sur-Yvette, France)

  • Francisco Alves

    (GeePs, Group of Electrical Engineering—Paris, UMR CNRS 8507, CentraleSupélec School, University of Paris-Saclay, Sorbonne University, 3 Rue Joliot-Curie, 91192 Gif-sur-Yvette, France)

Abstract

This paper presents the development of a global and integrated sizing approach under different performance indexes applied to fuel cell/battery hybrid power systems. The strong coupling between the hardware sizing process and the system supervision (energy management strategy EMS) makes it hard for the design to consider all the possibilities, and today’s methodologies are mostly experience-based approaches that are impervious to technological disruption. With a smart design approach, new technologies are easier to consider, and this approach facilitates the use of new technologies for transport applications with a decision help tool. An automotive application with a hybrid fuel cell (PEMFC)/battery (Li-Ion) is considered to develop this approach. The proposed approach is based on imbricated optimization loops and considers multiple criteria such as the fuel consumption, reliability, and volume of the architecture, in keeping with industry expectations to allow a good trade-off between different performance indexes and explore their design options. This constitutes a low computational time and a very effective support tool that allows limited overconsumption and lifetime reduction for designed architecture in extreme and non-optimal use. We obtain, thanks to this work, a pre-design tool that helps to realize the first conception choice.

Suggested Citation

  • Adriano Ceschia & Toufik Azib & Olivier Bethoux & Francisco Alves, 2022. "Multi-Criteria Optimal Design for FUEL Cell Hybrid Power Sources," Energies, MDPI, vol. 15(9), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3364-:d:808861
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3364/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3364/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rui Yang & Yupeng Yuan & Rushun Ying & Boyang Shen & Teng Long, 2020. "A Novel Energy Management Strategy for a Ship’s Hybrid Solar Energy Generation System Using a Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 13(6), pages 1-14, March.
    2. Tri Cuong Do & Hoai Vu Anh Truong & Hoang Vu Dao & Cong Minh Ho & Xuan Dinh To & Tri Dung Dang & Kyoung Kwan Ahn, 2019. "Energy Management Strategy of a PEM Fuel Cell Excavator with a Supercapacitor/Battery Hybrid Power Source," Energies, MDPI, vol. 12(22), pages 1-24, November.
    3. Junhui Liu & Lei Feng & Zhiwu Li, 2017. "The Optimal Road Grade Design for Minimizing Ground Vehicle Energy Consumption," Energies, MDPI, vol. 10(5), pages 1-31, May.
    4. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    5. Mehdi Sellali & Alexandre Ravey & Achour Betka & Abdellah Kouzou & Mohamed Benbouzid & Abdesslem Djerdir & Ralph Kennel & Mohamed Abdelrahem, 2022. "Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-17, February.
    6. Yi Yang & Zhihao Shang & Yao Chen & Yanhua Chen, 2020. "Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting," Energies, MDPI, vol. 13(3), pages 1-19, January.
    7. Song, Ziyou & Zhang, Xiaobin & Li, Jianqiu & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "Component sizing optimization of plug-in hybrid electric vehicles with the hybrid energy storage system," Energy, Elsevier, vol. 144(C), pages 393-403.
    8. Tran, Dai-Duong & Vafaeipour, Majid & El Baghdadi, Mohamed & Barrero, Ricardo & Van Mierlo, Joeri & Hegazy, Omar, 2020. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    9. 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.
    10. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert & Veneroni, Marco, 2017. "Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 158-187.
    11. Xueying Song & Hongyu Lin & Gejirifu De & Hanfang Li & Xiaoxu Fu & Zhongfu Tan, 2020. "An Energy Optimal Dispatching Model of an Integrated Energy System Based on Uncertain Bilevel Programming," Energies, MDPI, vol. 13(2), pages 1-24, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Ying Yang & Weige Zhang & Shaoyuan Wei & Zhenpo Wang, 2020. "Optimal Sizing of On-Board Energy Storage Systems and Stationary Charging Infrastructures for a Catenary-Free Tram," Energies, MDPI, vol. 13(23), pages 1-21, November.
    3. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    5. Reddi Khasim, Shaik & Dhanamjayulu, C., 2021. "Selection parameters and synthesis of multi-input converters for electric vehicles: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    6. Wang, Jing & Kang, Lixia & Liu, Yongzhong, 2020. "Optimal scheduling for electric bus fleets based on dynamic programming approach by considering battery capacity fade," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    7. 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).
    8. Zhou, Yu & Meng, Qiang & Ong, Ghim Ping, 2022. "Electric Bus Charging Scheduling for a Single Public Transport Route Considering Nonlinear Charging Profile and Battery Degradation Effect," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 49-75.
    9. Yongbing Xiang & Xiaomin Yang, 2021. "An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-21, February.
    10. Luigi Fortuna & Arturo Buscarino, 2022. "Sustainable Energy Systems," Energies, MDPI, vol. 15(23), pages 1-7, December.
    11. Pierpaolo Polverino & Ivan Arsie & Cesare Pianese, 2021. "Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon," Energies, MDPI, vol. 14(12), pages 1-11, June.
    12. Barelli, L. & Bidini, G. & Bonucci, F. & Castellini, L. & Fratini, A. & Gallorini, F. & Zuccari, A., 2019. "Flywheel hybridization to improve battery life in energy storage systems coupled to RES plants," Energy, Elsevier, vol. 173(C), pages 937-950.
    13. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    14. Shaobo Xie & Xiaosong Hu & Kun Lang & Shanwei Qi & Tong Liu, 2018. "Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    15. Yang Xia & Wenjia Zeng & Xinjie Xing & Yuanzhu Zhan & Kim Hua Tan & Ajay Kumar, 2023. "Joint optimisation of drone routing and battery wear for sustainable supply chain development: a mixed-integer programming model based on blockchain-enabled fleet sharing," Annals of Operations Research, Springer, vol. 327(1), pages 89-127, August.
    16. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    17. Xiaoming Yuan & Yao Lu & Jiusheng Bao & Peixin Han & Yan Yin & Xu Wang, 2022. "Design and Optimization on the Degree of Hybridization of Underground Hybrid Electric Trackless Rubber-Tyred Vehicle," Energies, MDPI, vol. 15(18), pages 1-20, September.
    18. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    19. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2020. "Network-level energy consumption estimation for electric vehicles considering vehicle and user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 30-46.
    20. Yi Zhang & Qiang Guo & Jie Song, 2023. "Internet-Distributed Hardware-in-the-Loop Simulation Platform for Plug-In Fuel Cell Hybrid Vehicles," Energies, MDPI, vol. 16(18), pages 1-17, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3364-:d:808861. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.