IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i15p4617-d604923.html
   My bibliography  Save this article

Comprehensive Review on Smart Techniques for Estimation of State of Health for Battery Management System Application

Author

Listed:
  • Sumukh Surya

    (ePowerTrain, Kirtane Pandit Information Technology (KPIT), Bangalore 560103, India)

  • Vidya Rao

    (Department of Information Science and Engineering, Jyothy Institute of Technology, Bangalore 560082, India)

  • Sheldon S. Williamson

    (Department of Electrical, Computer and Software Engineering, Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada)

Abstract

Electric Vehicles (EV) and Hybrid EV (HEV) use Lithium (Li) ion battery packs to drive them. These battery packs possess high specific density and low discharge rates. However, some of the limitations of such Li ion batteries are sensitivity to high temperature and health degradation over long usage. The Battery Management System (BMS) protects the battery against overvoltage, overcurrent etc., and monitors the State of Charge (SOC) and the State of Health (SOH). SOH is a complex phenomenon dealing with the effects related to aging of the battery such as the increase in the internal resistance and decrease in the capacity due to unwanted side reactions. The battery life can be extended by estimating the SOH accurately. In this paper, an extensive review on the effects of aging of the battery on the electrodes, effects of Solid Electrolyte Interface (SEI) deposition layer on the battery and the various techniques used for estimation of SOH are presented. This would enable prospective researchers to address the estimation of SOH with greater accuracy and reliability.

Suggested Citation

  • Sumukh Surya & Vidya Rao & Sheldon S. Williamson, 2021. "Comprehensive Review on Smart Techniques for Estimation of State of Health for Battery Management System Application," Energies, MDPI, vol. 14(15), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4617-:d:604923
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/15/4617/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/15/4617/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Sumukh Surya & Vinicius Marcis & Sheldon Williamson, 2020. "Core Temperature Estimation for a Lithium ion 18650 Cell," Energies, MDPI, vol. 14(1), pages 1-21, December.
    3. Zhonghua Yun & Wenhu Qin & Weipeng Shi & Peng Ping, 2020. "State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach," Energies, MDPI, vol. 13(18), pages 1-22, September.
    4. Zhengyu Liu & Jingjie Zhao & Hao Wang & Chao Yang, 2020. "A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs," Energies, MDPI, vol. 13(4), pages 1-17, February.
    5. 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.
    6. Shuxiang Song & Chen Fei & Haiying Xia, 2020. "Lithium-Ion Battery SOH Estimation Based on XGBoost Algorithm with Accuracy Correction," Energies, MDPI, vol. 13(4), pages 1-13, February.
    7. Henok Ayele Behabtu & Maarten Messagie & Thierry Coosemans & Maitane Berecibar & Kinde Anlay Fante & Abraham Alem Kebede & Joeri Van Mierlo, 2020. "A Review of Energy Storage Technologies’ Application Potentials in Renewable Energy Sources Grid Integration," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    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. Carlos Henrique Illa Font & Hugo Valadares Siqueira & João Eustáquio Machado Neto & João Lucas Ferreira dos Santos & Sergio Luiz Stevan & Attilio Converti & Fernanda Cristina Corrêa, 2023. "Second Life of Lithium-Ion Batteries of Electric Vehicles: A Short Review and Perspectives," Energies, MDPI, vol. 16(2), pages 1-14, January.
    2. Jikai Bi & Jae-Cheon Lee & Hao Liu, 2022. "Performance Comparison of Long Short-Term Memory and a Temporal Convolutional Network for State of Health Estimation of a Lithium-Ion Battery using Its Charging Characteristics," Energies, MDPI, vol. 15(7), pages 1-24, March.
    3. Maria Cortada-Torbellino & Abdelali El Aroudi & Hugo Valderrama-Blavi, 2023. "Outlook of Lithium-Ion Battery Regulations and Procedures to Improve Cell Degradation Detection and Other Alternatives," Energies, MDPI, vol. 16(5), pages 1-13, March.
    4. Zeinab Teimoori & Abdulsalam Yassine, 2022. "A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation," Sustainability, MDPI, vol. 14(21), pages 1-23, October.

    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. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    2. Hashemi, Seyed Reza & Mahajan, Ajay Mohan & Farhad, Siamak, 2021. "Online estimation of battery model parameters and state of health in electric and hybrid aircraft application," Energy, Elsevier, vol. 229(C).
    3. Eid Gul & Giorgio Baldinelli & Pietro Bartocci, 2022. "Energy Transition: Renewable Energy-Based Combined Heat and Power Optimization Model for Distributed Communities," Energies, MDPI, vol. 15(18), pages 1-18, September.
    4. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    5. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    6. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    7. Dalala, Zakariya & Al-Omari, Murad & Al-Addous, Mohammad & Bdour, Mathhar & Al-Khasawneh, Yaqoub & Alkasrawi, Malek, 2022. "Increased renewable energy penetration in national electrical grids constraints and solutions," Energy, Elsevier, vol. 246(C).
    8. Mei Zhang & Wanli Chen & Jun Yin & Tao Feng, 2022. "Health Factor Extraction of Lithium-Ion Batteries Based on Discrete Wavelet Transform and SOH Prediction Based on CatBoost," Energies, MDPI, vol. 15(15), pages 1-17, July.
    9. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. Akash Samanta & Sheldon S. Williamson, 2021. "A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems," Energies, MDPI, vol. 14(18), pages 1-25, September.
    11. Ethelbert Ezemobi & Mario Silvagni & Ahmad Mozaffari & Andrea Tonoli & Amir Khajepour, 2022. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions," Energies, MDPI, vol. 15(3), pages 1-20, February.
    12. Razzaqul Ahshan, 2021. "Potential and Economic Analysis of Solar-to-Hydrogen Production in the Sultanate of Oman," Sustainability, MDPI, vol. 13(17), pages 1-22, August.
    13. Lechón, Yolanda & Lago, Carmen & Herrera, Israel & Gamarra, Ana Rosa & Pérula, Alberto, 2023. "Carbon benefits of different energy storage alternative end uses. Application to the Spanish case," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    14. Kai Xu & Youguang Guo & Gang Lei & Jianguo Zhu, 2023. "A Review of Flywheel Energy Storage System Technologies," Energies, MDPI, vol. 16(18), pages 1-32, September.
    15. Giacomo Talluri & Gabriele Maria Lozito & Francesco Grasso & Carlos Iturrino Garcia & Antonio Luchetta, 2021. "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, MDPI, vol. 14(24), pages 1-23, December.
    16. Bertasini, Davide & Battista, Federico & Rizzioli, Fabio & Frison, Nicola & Bolzonella, David, 2023. "Decarbonization of the European natural gas grid using hydrogen and methane biologically produced from organic waste: A critical overview," Renewable Energy, Elsevier, vol. 206(C), pages 386-396.
    17. Panpan Hu & W. F. Tang & C. H. Li & Shu-Lun Mak & C. Y. Li & C. C. Lee, 2023. "Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network," Energies, MDPI, vol. 16(14), pages 1-19, July.
    18. Li, Jinwen & Deng, Zhongwei & Liu, Hongao & Xie, Yi & Liu, Chuan & Lu, Chen, 2022. "Battery capacity trajectory prediction by capturing the correlation between different vehicles," Energy, Elsevier, vol. 260(C).
    19. Hamdi Abdi, 2022. "A Brief Review of Microgrid Surveys, by Focusing on Energy Management System," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    20. Rafał Kuźniak & Artur Pawelec & Artur S. Bartosik & Marek Pawelczyk, 2022. "Determining the Power and Capacity of Electricity Storage in Cooperation with the Microgrid for the Implementation of the Price Arbitration Strategy of Industrial Enterprises Installation," Energies, MDPI, vol. 15(15), pages 1-18, August.

    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:15:p:4617-:d:604923. 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.