IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i10p2428-d1397576.html
   My bibliography  Save this article

AC Direct Charging for Electric Vehicles via a Reconfigurable Cascaded Multilevel Converter

Author

Listed:
  • Giulia Tresca

    (Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
    These authors contributed equally to this work.)

  • Pericle Zanchetta

    (Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
    These authors contributed equally to this work.)

Abstract

This paper presents a charging architecture for the Reconfigurable Cascaded Multilevel converter, which was specifically designed for electric vehicle (EV) powertrain applications. The RCMC topology is capable of executing power conversion and actively managing battery systems concurrently. The active battery management is achieved using the Reconfigurable Battery Module, which regulates the serial connection of cells via a switch pattern. In this paper, the RCMC is directly interfaced with an AC three-phase power system, facilitating the dynamic control over battery cells charging. Its inherent design allows for the implementation of various charging algorithms, customizable to specific requirements, without necessitating additional intermediary power stages. Firstly, an overview of the RCMC topology is given, and an analysis to define the optimal filter inductance is carried out. Subsequently, after the AC system characteristics are explained, two charging algorithms are presented and described: one prioritizes State of Charge (SOC) balancing among battery cells, while the other focuses on minimizing power losses. Moreover, a time estimation computation for the RCMC is carried out considering a two-level AC charging station. The result is compared with the time required for a conventional battery pack. The results show a reduction of 10 s in charging time for a mere 20% increase in SOC. Finally, the experimental setup is presented and used to validate the efficacy of the proposed algorithms.

Suggested Citation

  • Giulia Tresca & Pericle Zanchetta, 2024. "AC Direct Charging for Electric Vehicles via a Reconfigurable Cascaded Multilevel Converter," Energies, MDPI, vol. 17(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2428-:d:1397576
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/10/2428/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/10/2428/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Caiping Zhang & Jiuchun Jiang & Linjing Zhang & Sijia Liu & Leyi Wang & Poh Chiang Loh, 2016. "A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery," Energies, MDPI, vol. 9(11), pages 1-16, November.
    2. Filippo Gemma & Giulia Tresca & Andrea Formentini & Pericle Zanchetta, 2023. "Balanced Charging Algorithm for CHB in an EV Powertrain," Energies, MDPI, vol. 16(14), pages 1-15, July.
    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. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    2. Prarthana Pillai & Sneha Sundaresan & Pradeep Kumar & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Open-Circuit Voltage Models for Battery Management Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-25, September.
    3. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    4. Md Ohirul Qays & Yonis Buswig & Md Liton Hossain & Ahmed Abu-Siada, 2020. "Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System," Energies, MDPI, vol. 13(13), pages 1-16, July.
    5. Macdonald Nko & S.P. Daniel Chowdhury & Olawale Popoola, 2019. "Application Assessment of Pumped Storage and Lithium-Ion Batteries on Electricity Supply Grid," Energies, MDPI, vol. 12(15), pages 1-36, July.
    6. Woo-Yong Kim & Pyeong-Yeon Lee & Jonghoon Kim & Kyung-Soo Kim, 2019. "A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles," Energies, MDPI, vol. 12(17), pages 1-20, September.
    7. Bansal, Vishal & Kumar, Deepak Prakash & Roy, Debjit & Subramanian, Shankar C., 2022. "Performance evaluation and optimization of design parameters for electric vehicle-sharing platforms by considering vehicle dynamics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    8. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.
    9. Zhang, Caiping & Jiang, Yan & Jiang, Jiuchun & Cheng, Gong & Diao, Weiping & Zhang, Weige, 2017. "Study on battery pack consistency evolutions and equilibrium diagnosis for serial- connected lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 510-519.
    10. Torregrosa, Antonio José & Broatch, Alberto & Olmeda, Pablo & Agizza, Luca, 2023. "A generalized equivalent circuit model for lithium-iron phosphate batteries," Energy, Elsevier, vol. 284(C).
    11. S. N. Syed Nasir & J. J. Jamian & M. W. Mustafa, 2018. "Minimizing Harmonic Distortion Impact at Distribution System with Considering Large-Scale EV Load Behaviour Using Modified Lightning Search Algorithm and Pareto-Fuzzy Approach," Complexity, Hindawi, vol. 2018, pages 1-14, February.
    12. Wang, Limei & Sun, Jingjing & Cai, Yingfeng & Lian, Yubo & Jin, Mengjie & Zhao, Xiuliang & Wang, Ruochen & Chen, Long & Chen, Jun, 2023. "A novel OCV curve reconstruction and update method of lithium-ion batteries at different temperatures based on cloud data," Energy, Elsevier, vol. 268(C).
    13. Bharatiraja Chokkalingam & Sanjeevikumar Padmanaban & Pierluigi Siano & Ramesh Krishnamoorthy & Raghu Selvaraj, 2017. "Real-Time Forecasting of EV Charging Station Scheduling for Smart Energy Systems," Energies, MDPI, vol. 10(3), pages 1-16, March.
    14. Karimi, Danial & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2023. "A comprehensive coupled 0D-ECM to 3D-CFD thermal model for heat pipe assisted-air cooling thermal management system under fast charge and discharge," Applied Energy, Elsevier, vol. 339(C).
    15. Shun-Chung Wang & Zhi-Yao Zhang, 2023. "Research on Optimum Charging Current Profile with Multi-Stage Constant Current Based on Bio-Inspired Optimization Algorithms for Lithium-Ion Batteries," Energies, MDPI, vol. 16(22), pages 1-23, November.
    16. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    17. Hui Pang & Fengqi Zhang, 2018. "Experimental Data-Driven Parameter Identification and State of Charge Estimation for a Li-Ion Battery Equivalent Circuit Model," Energies, MDPI, vol. 11(5), pages 1-14, April.
    18. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    19. Ines Baccouche & Sabeur Jemmali & Bilal Manai & Noshin Omar & Najoua Essoukri Ben Amara, 2017. "Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(6), pages 1-22, May.
    20. Majid Astaneh & Jelena Andric & Lennart Löfdahl & Dario Maggiolo & Peter Stopp & Mazyar Moghaddam & Michel Chapuis & Henrik Ström, 2020. "Calibration Optimization Methodology for Lithium-Ion Battery Pack Model for Electric Vehicles in Mining Applications," Energies, MDPI, vol. 13(14), pages 1-27, July.

    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:17:y:2024:i:10:p:2428-:d:1397576. 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.