IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i11p2825-d366412.html
   My bibliography  Save this article

Battery Management Systems—Challenges and Some Solutions

Author

Listed:
  • Balakumar Balasingam

    (Department of Electrical and Computer Engineering, University of Windsor, 401 Sunset Ave., Office#3051, Windsor, ON N9B3P4, Canada)

  • Mostafa Ahmed

    (Department of Electrical and Computer Engineering, University of Windsor, 401 Sunset Ave., Office#3051, Windsor, ON N9B3P4, Canada)

  • Krishna Pattipati

    (Department of Electrical and Computer Engineering, University of Connecticut, 371 Fairfield Rd, Office#350, Storrs, CT 06269, USA)

Abstract

Electric vehicles are set to be the dominant form of transportation in the near future and Lithium-based rechargeable battery packs have been widely adopted in them. Battery packs need to be constantly monitored and managed in order to maintain the safety, efficiency and reliability of the overall electric vehicle system. A battery management system consists of a battery fuel gauge, optimal charging algorithm, and cell/thermal balancing circuitry. It uses three non-invasive measurements from the battery, voltage, current and temperature, in order to estimate crucial states and parameters of the battery system, such as battery impedance, battery capacity, state of charge, state of health, power fade, and remaining useful life. These estimates are important for the proper functioning of optimal charging algorithms, charge and thermal balancing strategies, and battery safety mechanisms. Approach to robust battery management consists of accurate characterization, robust estimation of battery states and parameters, and optimal battery control strategies. This paper describes some recent approaches developed by the authors towards developing a robust battery management system.

Suggested Citation

  • Balakumar Balasingam & Mostafa Ahmed & Krishna Pattipati, 2020. "Battery Management Systems—Challenges and Some Solutions," Energies, MDPI, vol. 13(11), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2825-:d:366412
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/11/2825/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/11/2825/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Waag, Wladislaw & Sauer, Dirk Uwe, 2013. "Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination," Applied Energy, Elsevier, vol. 111(C), pages 416-427.
    2. Xiong, Rui & Sun, Fengchun & Gong, Xianzhi & Gao, Chenchen, 2014. "A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 1421-1433.
    3. Ahmed, Mostafa Shaban & Raihan, Sheikh Arif & Balasingam, Balakumar, 2020. "A scaling approach for improved state of charge representation in rechargeable batteries," Applied Energy, Elsevier, vol. 267(C).
    4. Taichun Qin & Shengkui Zeng & Jianbin Guo & Zakwan Skaf, 2016. "A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena," Energies, MDPI, vol. 9(11), pages 1-18, November.
    5. Deng, Yuanwang & Ying, Hejie & E, Jiaqiang & Zhu, Hao & Wei, Kexiang & Chen, Jingwei & Zhang, Feng & Liao, Gaoliang, 2019. "Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries," Energy, Elsevier, vol. 176(C), pages 91-102.
    6. Wang, Zhenpo & Hong, Jichao & Liu, Peng & Zhang, Lei, 2017. "Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles," Applied Energy, Elsevier, vol. 196(C), pages 289-302.
    7. Heymans, Catherine & Walker, Sean B. & Young, Steven B. & Fowler, Michael, 2014. "Economic analysis of second use electric vehicle batteries for residential energy storage and load-levelling," Energy Policy, Elsevier, vol. 71(C), pages 22-30.
    8. Kang, Yongzhe & Duan, Bin & Zhou, Zhongkai & Shang, Yunlong & Zhang, Chenghui, 2020. "Online multi-fault detection and diagnosis for battery packs in electric vehicles," Applied Energy, Elsevier, vol. 259(C).
    9. Avvari, G.V. & Pattipati, B. & Balasingam, B. & Pattipati, K.R. & Bar-Shalom, Y., 2015. "Experimental set-up and procedures to test and validate battery fuel gauge algorithms," Applied Energy, Elsevier, vol. 160(C), pages 404-418.
    10. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
    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. Ospina Agudelo, Brian & Zamboni, Walter & Postiglione, Fabio & Monmasson, Eric, 2023. "Battery State-of-Health estimation based on multiple charge and discharge features," Energy, Elsevier, vol. 263(PA).
    2. Kiarash Movassagh & Arif Raihan & Balakumar Balasingam & Krishna Pattipati, 2021. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries," Energies, MDPI, vol. 14(14), pages 1-33, July.
    3. Sneha Sundaresan & Bharath Chandra Devabattini & Pradeep Kumar & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Tabular Open Circuit Voltage Modelling of Li-Ion Batteries for Robust SOC Estimation," Energies, MDPI, vol. 15(23), pages 1-23, December.
    4. Okay, Kamil & Eray, Sermet & Eray, Aynur, 2022. "Development of prototype battery management system for PV system," Renewable Energy, Elsevier, vol. 181(C), pages 1294-1304.
    5. Luigi Sequino & Ezio Mancaruso & Bianca Maria Vaglieco, 2022. "Analogies in the Analysis of the Thermal Status of Batteries and Internal Combustion Engines for Mobility," Energies, MDPI, vol. 15(7), pages 1-20, April.
    6. Prarthana Pillai & Sneha Sundaresan & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer–Rao Lower Bound," Energies, MDPI, vol. 15(22), pages 1-21, November.
    7. Ashleigh Townsend & Rupert Gouws, 2023. "A Comparative Review of Capacity Measurement in Energy Storage Devices," Energies, MDPI, vol. 16(10), pages 1-26, May.
    8. Thomas F. Landinger & Guenter Schwarzberger & Guenter Hofer & Matthias Rose & Andreas Jossen, 2021. "Power Line Communications for Automotive High Voltage Battery Systems: Channel Modeling and Coexistence Study with Battery Monitoring," Energies, MDPI, vol. 14(7), pages 1-26, March.
    9. 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.

    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. 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.
    2. Avvari, G.V. & Pattipati, B. & Balasingam, B. & Pattipati, K.R. & Bar-Shalom, Y., 2015. "Experimental set-up and procedures to test and validate battery fuel gauge algorithms," Applied Energy, Elsevier, vol. 160(C), pages 404-418.
    3. Xu Lei & Xi Zhao & Guiping Wang & Weiyu Liu, 2019. "A Novel Temperature–Hysteresis Model for Power Battery of Electric Vehicles with an Adaptive Joint Estimator on State of Charge and Power," Energies, MDPI, vol. 12(19), pages 1-24, September.
    4. 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.
    5. Ahmed, Mostafa Shaban & Raihan, Sheikh Arif & Balasingam, Balakumar, 2020. "A scaling approach for improved state of charge representation in rechargeable batteries," Applied Energy, Elsevier, vol. 267(C).
    6. 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.
    7. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    8. Fan Zhang & Xiao Zheng & Zixuan Xing & Minghu Wu, 2024. "Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features," Energies, MDPI, vol. 17(7), pages 1-21, March.
    9. Wang, Tao & Tseng, K.J. & Zhao, Jiyun & Wei, Zhongbao, 2014. "Thermal investigation of lithium-ion battery module with different cell arrangement structures and forced air-cooling strategies," Applied Energy, Elsevier, vol. 134(C), pages 229-238.
    10. Kiarash Movassagh & Arif Raihan & Balakumar Balasingam & Krishna Pattipati, 2021. "A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries," Energies, MDPI, vol. 14(14), pages 1-33, July.
    11. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2021. "Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data," Energy, Elsevier, vol. 225(C).
    12. Sun, Zhenyu & Han, Yang & Wang, Zhenpo & Chen, Yong & Liu, Peng & Qin, Zian & Zhang, Zhaosheng & Wu, Zhiqiang & Song, Chunbao, 2022. "Detection of voltage fault in the battery system of electric vehicles using statistical analysis," Applied Energy, Elsevier, vol. 307(C).
    13. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    14. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    15. Bai, Guangxing & Wang, Pingfeng & Hu, Chao & Pecht, Michael, 2014. "A generic model-free approach for lithium-ion battery health management," Applied Energy, Elsevier, vol. 135(C), pages 247-260.
    16. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).
    17. Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
    18. Bing Jiang & Zeqi Chen & Feifan Chen, 2019. "Influence of Sampling Delay on the Estimation of Lithium-Ion Battery Parameters and an Optimized Estimation Method," Energies, MDPI, vol. 12(10), pages 1-16, May.
    19. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    20. Abdel Monem, Mohamed & Trad, Khiem & Omar, Noshin & Hegazy, Omar & Mantels, Bart & Mulder, Grietus & Van den Bossche, Peter & Van Mierlo, Joeri, 2015. "Lithium-ion batteries: Evaluation study of different charging methodologies based on aging process," Applied Energy, Elsevier, vol. 152(C), pages 143-155.

    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:13:y:2020:i:11:p:2825-:d:366412. 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.