IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i14p5565-d1200560.html
   My bibliography  Save this article

Balanced Charging Algorithm for CHB in an EV Powertrain

Author

Listed:
  • Filippo Gemma

    (Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy)

  • Giulia Tresca

    (Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy)

  • Andrea Formentini

    (Department of Marine, Electrical, Electronics and Telecommunication Engineering, University of Genova, 16126 Genova, Italy)

  • Pericle Zanchetta

    (Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
    Department of Electrical and Electronic Engineering, University of Nottingham, Nottingham NG7 2RD, UK)

Abstract

The scientific literature acknowledges cascaded H-bridge (CHB) converters as a viable alternative to two-level inverters in electric vehicle (EV) powertrain applications. In the context of an electric vehicle engine connected to a DC charger, this study introduces a state of charge (SOC)-governed method for charging li-ion battery modules using a cascaded H-bridge converter. The key strength of this algorithm lies in its ability to achieve balanced charging of battery modules across all three-phase submodules while simultaneously controlling the DC charger, eliminating the need for an additional intermediate converter. Moreover, the algorithm is highly customizable, allowing adaptation to various configurations involving different numbers of submodules per phase. Simulative and experimental results are presented to demonstrate the effectiveness of the proposed charging algorithm, validating its practical application.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5565-:d:1200560
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/14/5565/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/14/5565/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ming-Hui Chang & Han-Pang Huang & Shu-Wei Chang, 2013. "A New State of Charge Estimation Method for LiFePO 4 Battery Packs Used in Robots," Energies, MDPI, vol. 6(4), pages 1-24, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    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. Sandoval, Cinda & Alvarado, Victor M. & Carmona, Jean-Claude & Lopez Lopez, Guadalupe & Gomez-Aguilar, J.F., 2017. "Energy management control strategy to improve the FC/SC dynamic behavior on hybrid electric vehicles: A frequency based distribution," Renewable Energy, Elsevier, vol. 105(C), pages 407-418.
    2. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    3. F. Isorna Llerena & E. López González & J. J. Caparrós Mancera & F. Segura Manzano & J. M. Andújar, 2021. "Hydrogen vs. Battery-Based Propulsion Systems in Unipersonal Vehicles—Developing Solutions to Improve the Sustainability of Urban Mobility," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    4. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    5. Cuma, Mehmet Ugras & Koroglu, Tahsin, 2015. "A comprehensive review on estimation strategies used in hybrid and battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 517-531.
    6. Van Quan Dao & Minh-Chau Dinh & Chang Soon Kim & Minwon Park & Chil-Hoon Doh & Jeong Hyo Bae & Myung-Kwan Lee & Jianyong Liu & Zhiguo Bai, 2021. "Design of an Effective State of Charge Estimation Method for a Lithium-Ion Battery Pack Using Extended Kalman Filter and Artificial Neural Network," Energies, MDPI, vol. 14(9), pages 1-20, May.
    7. Ashikur Rahman & Xianke Lin & Chongming Wang, 2022. "Li-Ion Battery Anode State of Charge Estimation and Degradation Monitoring Using Battery Casing via Unknown Input Observer," Energies, MDPI, vol. 15(15), pages 1-19, August.
    8. Lyons, P.F. & Wade, N.S. & Jiang, T. & Taylor, P.C. & Hashiesh, F. & Michel, M. & Miller, D., 2015. "Design and analysis of electrical energy storage demonstration projects on UK distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 677-691.
    9. Nimalsiri, Nanduni I. & Ratnam, Elizabeth L. & Mediwaththe, Chathurika P. & Smith, David B. & Halgamuge, Saman K., 2021. "Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit," Applied Energy, Elsevier, vol. 291(C).
    10. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    11. Fathy, Ahmed & Ferahtia, Seydali & Rezk, Hegazy & Yousri, Dalia & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid," Energy, Elsevier, vol. 247(C).
    12. Angelo Bonfitto, 2020. "A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks," Energies, MDPI, vol. 13(10), pages 1-13, May.
    13. Nicolás Müller & Samir Kouro & Pericle Zanchetta & Patrick Wheeler & Gustavo Bittner & Francesco Girardi, 2019. "Energy Storage Sizing Strategy for Grid-Tied PV Plants under Power Clipping Limitations," Energies, MDPI, vol. 12(9), pages 1-17, May.
    14. Javidsharifi, Mahshid & Niknam, Taher & Aghaei, Jamshid & Mokryani, Geev, 2018. "Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices," Applied Energy, Elsevier, vol. 216(C), pages 367-381.
    15. Józef Pszczółkowski, 2021. "Description of Acid Battery Operating Parameters," Energies, MDPI, vol. 14(21), pages 1-17, November.
    16. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    17. Angelo Maiorino & Adrián Mota-Babiloni & Fabio Petruzziello & Manuel Gesù Del Duca & Andrea Ariano & Ciro Aprea, 2022. "A Comprehensive Energy Model for an Optimal Design of a Hybrid Refrigerated Van," Energies, MDPI, vol. 15(13), pages 1-23, July.
    18. Alberto Pajares & Xavier Blasco & Juan Manuel Herrero & Raúl Simarro, 2017. "Using a Multiobjective Approach to Compare Multiple Design Alternatives—An Application to Battery Dynamic Model Tuning," Energies, MDPI, vol. 10(7), pages 1-19, July.
    19. Perugu, Harikishan & Collier, Sonya & Tan, Yi & Yoon, Seungju & Herner, Jorn, 2023. "Characterization of battery electric transit bus energy consumption by temporal and speed variation," Energy, Elsevier, vol. 263(PC).
    20. Lin, Cheng & Yu, Quanqing & Xiong, Rui & Wang, Le Yi, 2017. "A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 205(C), pages 892-902.

    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:16:y:2023:i:14:p:5565-:d:1200560. 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.