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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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    References listed on IDEAS

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    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. 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).
    7. 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).
    8. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    9. 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.
    10. 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).
    11. 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.
    12. 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.
    13. 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).
    14. 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).

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