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Critical review of non-invasive diagnosis techniques for quantification of degradation modes in lithium-ion batteries

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  • Pastor-Fernández, Carlos
  • Yu, Tung Fai
  • Widanage, W. Dhammika
  • Marco, James

Abstract

Understanding the root causes of Lithium-ion battery degradation is a challenging task due to the complexity of the different mechanisms involved. For simplicity, ageing mechanisms are often grouped into three degradation modes (DMs): conductivity loss, loss of active material and loss of lithium inventory. Battery Management Systems (BMSs) do not currently include an indication of the underlying DMs causing the degradation. Pseudo Open Circuit Voltage (pOCV), Incremental Capacity - Differential Voltage (IC-DV), Electrochemical Impedance Spectroscopy and Differential Thermal Voltammetry are the most common non-invasive diagnosis techniques studied in the literature to quantify DMs. This work presents a critical and systematic review of these techniques with the focus on the elaboration of their strengths and weaknesses for the implementation in automotive applications. Firstly, each technique is classified into different groups and their working principles are presented. Secondly, an evaluation criterion is introduced to review each technique following a systematic approach. The comparison of the techniques highlight that pOCV and IC-DV are the most advantageous because they fulfill most of the points included in the evaluation criteria. The further implementation of these techniques would support battery lifetime control strategies and battery designs.

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  • Pastor-Fernández, Carlos & Yu, Tung Fai & Widanage, W. Dhammika & Marco, James, 2019. "Critical review of non-invasive diagnosis techniques for quantification of degradation modes in lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 138-159.
  • Handle: RePEc:eee:rensus:v:109:y:2019:i:c:p:138-159
    DOI: 10.1016/j.rser.2019.03.060
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    1. Limhi Somerville & James Michael Hooper & James Marco & Andrew McGordon & Chris Lyness & Marc Walker & Paul Jennings, 2017. "Impact of Vibration on the Surface Film of Lithium-Ion Cells," Energies, MDPI, vol. 10(6), pages 1-12, May.
    2. Chi Zhang & Fuwu Yan & Changqing Du & Jianqiang Kang & Richard Fiifi Turkson, 2017. "Evaluating the Degradation Mechanism and State of Health of LiFePO 4 Lithium-Ion Batteries in Real-World Plug-in Hybrid Electric Vehicles Application for Different Ageing Paths," Energies, MDPI, vol. 10(1), pages 1-13, January.
    3. Wang, Limei & Pan, Chaofeng & Liu, Liang & Cheng, Yong & Zhao, Xiuliang, 2016. "On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis," Applied Energy, Elsevier, vol. 168(C), pages 465-472.
    4. Weng, Caihao & Feng, Xuning & Sun, Jing & Peng, Huei, 2016. "State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking," Applied Energy, Elsevier, vol. 180(C), pages 360-368.
    5. Mingant, R. & Bernard, J. & Sauvant-Moynot, V., 2016. "Novel state-of-health diagnostic method for Li-ion battery in service," Applied Energy, Elsevier, vol. 183(C), pages 390-398.
    6. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    7. Ouyang, Minggao & Feng, Xuning & Han, Xuebing & Lu, Languang & Li, Zhe & He, Xiangming, 2016. "A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery," Applied Energy, Elsevier, vol. 165(C), pages 48-59.
    8. Yan, Dongxiang & Lu, Languang & Li, Zhe & Feng, Xuning & Ouyang, Minggao & Jiang, Fachao, 2016. "Durability comparison of four different types of high-power batteries in HEV and their degradation mechanism analysis," Applied Energy, Elsevier, vol. 179(C), pages 1123-1130.
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    Cited by:

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    3. Wei, Meng & Ye, Min & Zhang, Chuanwei & Li, Yan & Zhang, Jiale & Wang, Qiao, 2023. "A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling," Energy, Elsevier, vol. 283(C).
    4. Dongcheul Lee & Boram Koo & Chee Burm Shin & So-Yeon Lee & Jinju Song & Il-Chan Jang & Jung-Je Woo, 2019. "Modeling the Effect of the Loss of Cyclable Lithium on the Performance Degradation of a Lithium-Ion Battery," Energies, MDPI, vol. 12(22), pages 1-14, November.
    5. George Baure & Matthieu Dubarry, 2020. "Durability and Reliability of EV Batteries under Electric Utility Grid Operations: Impact of Frequency Regulation Usage on Cell Degradation," Energies, MDPI, vol. 13(10), pages 1-11, May.
    6. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).
    7. Shida Jiang & Zhengxiang Song, 2021. "Estimating the State of Health of Lithium-Ion Batteries with a High Discharge Rate through Impedance," Energies, MDPI, vol. 14(16), pages 1-20, August.
    8. Sun, Li & Li, Guanru & You, Fengqi, 2020. "Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    9. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    10. Salvatore Micari & Salvatore Foti & Antonio Testa & Salvatore De Caro & Francesco Sergi & Laura Andaloro & Davide Aloisio & Salvatore Gianluca Leonardi & Giuseppe Napoli, 2022. "Effect of WLTP CLASS 3B Driving Cycle on Lithium-Ion Battery for Electric Vehicles," Energies, MDPI, vol. 15(18), pages 1-25, September.
    11. Huang, Peifeng & Zeng, Ganghui & He, Yanyun & Liu, Shoutong & Li, Eric & Bai, Zhonghao, 2023. "Damage evolution mechanism and early warning using long short-term memory networks for battery slight overcharge cycles," Renewable Energy, Elsevier, vol. 217(C).
    12. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
    13. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    14. Roberta Cappabianca & Paolo De Angelis & Matteo Fasano & Eliodoro Chiavazzo & Pietro Asinari, 2023. "An Overview on Transport Phenomena within Solid Electrolyte Interphase and Their Impact on the Performance and Durability of Lithium-Ion Batteries," Energies, MDPI, vol. 16(13), pages 1-30, June.
    15. Wei, Meng & Ye, Min & Zhang, Chuanwei & Wang, Qiao & Lian, Gaoqi & Xia, Baozhou, 2024. "Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling," Energy, Elsevier, vol. 296(C).
    16. Mayyas, Ahmad & Chadly, Assia & Amer, Saed Talib & Azar, Elie, 2022. "Economics of the Li-ion batteries and reversible fuel cells as energy storage systems when coupled with dynamic electricity pricing schemes," Energy, Elsevier, vol. 239(PA).
    17. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    18. Liu, Yisheng & Fan, Guodong & Zhou, Boru & Chen, Shun & Sun, Ziqiang & Wang, Yansong & Zhang, Xi, 2023. "Rapid and flexible battery capacity estimation using random short-time charging segments based on residual convolutional networks," Applied Energy, Elsevier, vol. 351(C).
    19. Eirik Odinsen & Mahshid N. Amiri & Odne S. Burheim & Jacob J. Lamb, 2024. "Estimation of Differential Capacity in Lithium-Ion Batteries Using Machine Learning Approaches," Energies, MDPI, vol. 17(19), pages 1-15, October.

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