IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p8037-d693047.html
   My bibliography  Save this article

Performance Analysis of Commercial Passive Balancing Battery Management System Operation Using a Hardware-in-the-Loop Testbed

Author

Listed:
  • Asadullah Khalid

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

  • Alexander Stevenson

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

  • Arif I. Sarwat

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

Abstract

With increased usage, individual batteries within the battery pack will begin to show disparate voltage and State of Charge (SOC) profiles, which will impact the time at which batteries become balanced. Commercial battery management systems (BMSs), used in electric vehicles (EVs) and microgrids, typically send out signals suggesting removal of individual batteries or entire packs to prevent thermal runaway scenarios. To reuse these batteries, this paper presents an analysis of an off-the-shelf Orion BMS with a constrained cycling approach to assess the voltage and SOC balancing and thermal performances of such near-to-second life batteries. A scaled-down pack of series-connected batteries in 6s1p and 6s2p topologies are cycled through a combination of US06 drive and constant charge (CC) profiles using an OPAL-RT real-time Hardware-in-the-loop (HIL) simulator. These results are compared with those obtained from the Matlab/Simulink model to present the error incurred in the simulation environment. Results suggest that the close-to-second life batteries can be reused if operated in a constrained manner and that a scaled-up battery pack topology reduces incurred error.

Suggested Citation

  • Asadullah Khalid & Alexander Stevenson & Arif I. Sarwat, 2021. "Performance Analysis of Commercial Passive Balancing Battery Management System Operation Using a Hardware-in-the-Loop Testbed," Energies, MDPI, vol. 14(23), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8037-:d:693047
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/8037/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/23/8037/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Xiaowei & Cai, Yishan & Yang, Lin & Deng, Zhongwei & Qiang, Jiaxi, 2017. "State of charge estimation based on a new dual-polarization-resistance model for electric vehicles," Energy, Elsevier, vol. 135(C), pages 40-52.
    2. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    3. Temitayo O. Olowu & Aditya Sundararajan & Masood Moghaddami & Arif I. Sarwat, 2018. "Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey," Energies, MDPI, vol. 11(7), pages 1-32, July.
    4. Martinez-Laserna, E. & Gandiaga, I. & Sarasketa-Zabala, E. & Badeda, J. & Stroe, D.-I. & Swierczynski, M. & Goikoetxea, A., 2018. "Battery second life: Hype, hope or reality? A critical review of the state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 701-718.
    5. 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).
    6. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
    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. Chein-Chung Sun & Chun-Hung Chou & Yu-Liang Lin & Yu-Hua Huang, 2022. "A Cost-Effective Passive/Active Hybrid Equalizer Circuit Design," Energies, MDPI, vol. 15(6), pages 1-20, March.

    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. Xian Zhao & Siqi Wang & Xiaoyue Wang, 2018. "Characteristics and Trends of Research on New Energy Vehicle Reliability Based on the Web of Science," Sustainability, MDPI, vol. 10(10), pages 1-25, October.
    2. Yashraj Tripathy & Andrew McGordon & Chee Tong John Low, 2018. "A New Consideration for Validating Battery Performance at Low Ambient Temperatures," Energies, MDPI, vol. 11(9), pages 1-16, September.
    3. 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.
    4. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.
    5. Panchal, S. & Dincer, I. & Agelin-Chaab, M. & Fraser, R. & Fowler, M., 2016. "Experimental and simulated temperature variations in a LiFePO4-20Ah battery during discharge process," Applied Energy, Elsevier, vol. 180(C), pages 504-515.
    6. Choi, Mingi & Cha, Junepyo & Song, Jingeun, 2024. "Analysis of fuel economy reduction factors of hybrid electric vehicles in winter using on-road driving data," Energy, Elsevier, vol. 289(C).
    7. Antonio Capuano & Matteo Spano & Alessia Musa & Gianluca Toscano & Daniela Anna Misul, 2021. "Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-14, August.
    8. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    10. 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).
    11. Alexandros Nikolian & Yousef Firouz & Rahul Gopalakrishnan & Jean-Marc Timmermans & Noshin Omar & Peter Van den Bossche & Joeri Van Mierlo, 2016. "Lithium Ion Batteries—Development of Advanced Electrical Equivalent Circuit Models for Nickel Manganese Cobalt Lithium-Ion," Energies, MDPI, vol. 9(5), pages 1-23, May.
    12. Sandra Castano-Solis & Daniel Serrano-Jimenez & Lucia Gauchia & Javier Sanz, 2017. "The Influence of BMSs on the Characterization and Modeling of Series and Parallel Li-Ion Packs," Energies, MDPI, vol. 10(3), pages 1-13, February.
    13. 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.
    14. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    15. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    16. Ming Cai & Weijie Chen & Xiaojun Tan, 2017. "Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model," Energies, MDPI, vol. 10(10), pages 1-16, October.
    17. Motoaki, Yutaka & Yi, Wenqi & Salisbury, Shawn, 2018. "Empirical analysis of electric vehicle fast charging under cold temperatures," Energy Policy, Elsevier, vol. 122(C), pages 162-168.
    18. Ragab El-Sehiemy & Mohamed A. Hamida & Ehab Elattar & Abdullah Shaheen & Ahmed Ginidi, 2022. "Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm," Energies, MDPI, vol. 15(13), pages 1-20, June.
    19. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    20. Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.

    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:14:y:2021:i:23:p:8037-:d:693047. 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.