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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
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    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.

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