IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i1p146-d1558783.html
   My bibliography  Save this article

A Modular Step-Up DC–DC Converter Based on Dual-Isolated SEPIC/Cuk for Electric Vehicle Applications

Author

Listed:
  • Ahmed Darwish

    (School of Engineering, Lancaster University, Lancaster LA1 4YW, UK)

  • George A. Aggidis

    (School of Engineering, Lancaster University, Lancaster LA1 4YW, UK)

Abstract

Fuel cells (FCs) offer several operational advantages when integrated as a power source in electric vehicles (EVs). Since the voltage of these cells is typically low, usually less than 1 V, the power conversion system requires a DC–DC converter capable of providing a high voltage conversion ratio to match the input voltage of the motor propulsion system, which can exceed 400 V and reach up to 800 V. The modular DC–DC boost converter proposed in this paper is designed to achieve a high voltage step-up ratio for the input FC voltages through the use of isolated series-connecting boosting submodules connected. The power electronic topology employed in the submodules (SMs) is designed to provide a flexible output voltage while maintaining a continuous input current from the fuel cells with minimal current ripple to improve the FC’s performance. The proposed step-up modular converter provides several benefits including scalability, better controllability, and improved reliability, especially in the presence of partial faults. Computer simulations using MATLAB/SIMULINK ® software (R2024a) have been used to study the feasibility of the proposed converter when connected to a permanent magnet synchronous motor (PMSM). Also, experimental results using a 1 kW prototype composed of four SMs have been obtained to validate the performance of the proposed converter.

Suggested Citation

  • Ahmed Darwish & George A. Aggidis, 2025. "A Modular Step-Up DC–DC Converter Based on Dual-Isolated SEPIC/Cuk for Electric Vehicle Applications," Energies, MDPI, vol. 18(1), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:1:p:146-:d:1558783
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/1/146/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/1/146/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abdullah Aljumah & Ahmed Darwish & Denes Csala & Peter Twigg, 2024. "A Review on the Allocation of Sustainable Distributed Generators with Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 16(15), pages 1-17, July.
    2. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    3. Ioan-Sorin Sorlei & Nicu Bizon & Phatiphat Thounthong & Mihai Varlam & Elena Carcadea & Mihai Culcer & Mariana Iliescu & Mircea Raceanu, 2021. "Fuel Cell Electric Vehicles—A Brief Review of Current Topologies and Energy Management Strategies," Energies, MDPI, vol. 14(1), pages 1-29, January.
    4. Faissal Jelti & Amine Allouhi & Kheira Anissa Tabet Aoul, 2023. "Transition Paths towards a Sustainable Transportation System: A Literature Review," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
    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. Georgios Varvoutis & Athanasios Lampropoulos & Evridiki Mandela & Michalis Konsolakis & George E. Marnellos, 2022. "Recent Advances on CO 2 Mitigation Technologies: On the Role of Hydrogenation Route via Green H 2," Energies, MDPI, vol. 15(13), pages 1-38, June.
    2. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2023. "A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience," Energy, Elsevier, vol. 282(C).
    3. Jong-Wook Kim & Heungju Ahn & Hyeon Cheol Seo & Sang Cheol Lee, 2022. "Optimization of Solar/Fuel Cell Hybrid Energy System Using the Combinatorial Dynamic Encoding Algorithm for Searches (cDEAS)," Energies, MDPI, vol. 15(8), pages 1-15, April.
    4. Shangzhe Yu & Dominik Schäfer & Shidong Zhang & Roland Peters & Felix Kunz & Rüdiger-A. Eichel, 2023. "A Three-Dimensional Time-Dependent Model of the Degradation Caused by Chromium Poisoning in a Solid Oxide Fuel Cell Stack," Energies, MDPI, vol. 16(23), pages 1-23, November.
    5. Matthieu Matignon & Toufik Azib & Mehdi Mcharek & Ahmed Chaibet & Adriano Ceschia, 2023. "Real-Time Integrated Energy Management Strategy Applied to Fuel Cell Hybrid Systems," Energies, MDPI, vol. 16(6), pages 1-21, March.
    6. Li, Yichao & Ma, Chen & Liu, Kailong & Chang, Long & Zhang, Chenghui & Duan, Bin, 2024. "A novel joint estimation for core temperature and state of charge of lithium-ion battery based on classification approach and convolutional neural network," Energy, Elsevier, vol. 308(C).
    7. Abdulgader Alsharif & Chee Wei Tan & Razman Ayop & Ahmed Al Smin & Abdussalam Ali Ahmed & Farag Hamed Kuwil & Mohamed Mohamed Khaleel, 2023. "Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm," Energies, MDPI, vol. 16(3), pages 1-22, January.
    8. Marcin Kalinowski & Rafał Koba & Patryk Lipka & Krzysztof Czaplewski & Adam Weintrit & Joanna Witkowska, 2024. "Exploring External Costs on the Example of Sea–Land Transport Chains of Refrigerated Cargo Between Spain and Poland," Sustainability, MDPI, vol. 17(1), pages 1-22, December.
    9. Wang, Aijia & Wang, Junqi & Zhang, Ruijun & Cao, Shi-Jie, 2024. "Mitigating urban heat and air pollution considering green and transportation infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
    10. Wu, Jingda & Huang, Chao & He, Hongwen & Huang, Hailong, 2024. "Confidence-aware reinforcement learning for energy management of electrified vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    11. Changqing Du & Shiyang Huang & Yuyao Jiang & Dongmei Wu & Yang Li, 2022. "Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 15(12), pages 1-25, June.
    12. Mololuwa Akinyemi & Ekene Cynthia Onukwulu, 2025. "Conceptual Framework for Sustainability in Action: Resource Management and Green Practices in Hospitality and Logistics," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 736-744, January.
    13. Francesco Mocera & Aurelio Somà & Salvatore Martelli & Valerio Martini, 2023. "Trends and Future Perspective of Electrification in Agricultural Tractor-Implement Applications," Energies, MDPI, vol. 16(18), pages 1-36, September.
    14. Nicu Bizon & Phatiphat Thounthong, 2021. "A Simple and Safe Strategy for Improving the Fuel Economy of a Fuel Cell Vehicle," Mathematics, MDPI, vol. 9(6), pages 1-29, March.
    15. Khadijeh Hooshyari & Bahman Amini Horri & Hamid Abdoli & Mohsen Fallah Vostakola & Parvaneh Kakavand & Parisa Salarizadeh, 2021. "A Review of Recent Developments and Advanced Applications of High-Temperature Polymer Electrolyte Membranes for PEM Fuel Cells," Energies, MDPI, vol. 14(17), pages 1-38, September.
    16. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    17. Ahmed Darwish, 2024. "A Modular Step-Up DC/DC Converter for Electric Vehicles," Energies, MDPI, vol. 17(24), pages 1-22, December.
    18. Liviu I. Scurtu & Ioan Szabo & Marius Gheres, 2023. "Numerical Analysis of Crashworthiness on Electric Vehicle’s Battery Case with Auxetic Structure," Energies, MDPI, vol. 16(15), pages 1-18, August.
    19. Lu, Dagang & Yi, Fengyan & Hu, Donghai & Li, Jianwei & Yang, Qingqing & Wang, Jing, 2023. "Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy," Applied Energy, Elsevier, vol. 348(C).
    20. Peng Yin & Jinzhou Chen & Hongwen He, 2023. "Control of Oxygen Excess Ratio for a PEMFC Air Supply System by Intelligent PID Methods," Sustainability, MDPI, vol. 15(11), pages 1-20, May.

    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:18:y:2025:i:1:p:146-:d:1558783. 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.