IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i23p9165-d992066.html
   My bibliography  Save this article

Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review

Author

Listed:
  • Wei Li

    (State-Province Joint Engineering Laboratory of Power Source Technology for New Energy Vehicle, State Key Laboratory of Physical Chemistry of Solid Surfaces, Engineering Research Center of Electrochemical Technology, Collaborative Innovation Center of Chemistry for Energy Materials, College of Chemistry and Chemical Engineering, Ministry of Education, Xiamen University, Xiamen 361005, China)

  • Hang Li

    (State-Province Joint Engineering Laboratory of Power Source Technology for New Energy Vehicle, State Key Laboratory of Physical Chemistry of Solid Surfaces, Engineering Research Center of Electrochemical Technology, Collaborative Innovation Center of Chemistry for Energy Materials, College of Chemistry and Chemical Engineering, Ministry of Education, Xiamen University, Xiamen 361005, China)

  • Zheng He

    (College of Energy & School of Energy Research, Xiamen University, Xiamen 361102, China)

  • Weijie Ji

    (State-Province Joint Engineering Laboratory of Power Source Technology for New Energy Vehicle, State Key Laboratory of Physical Chemistry of Solid Surfaces, Engineering Research Center of Electrochemical Technology, Collaborative Innovation Center of Chemistry for Energy Materials, College of Chemistry and Chemical Engineering, Ministry of Education, Xiamen University, Xiamen 361005, China)

  • Jing Zeng

    (State-Province Joint Engineering Laboratory of Power Source Technology for New Energy Vehicle, State Key Laboratory of Physical Chemistry of Solid Surfaces, Engineering Research Center of Electrochemical Technology, Collaborative Innovation Center of Chemistry for Energy Materials, College of Chemistry and Chemical Engineering, Ministry of Education, Xiamen University, Xiamen 361005, China)

  • Xue Li

    (National and Local Joint Engineering Laboratory for Lithium-Ion Batteries and Materials Preparation Technology, Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Yiyong Zhang

    (National and Local Joint Engineering Laboratory for Lithium-Ion Batteries and Materials Preparation Technology, Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China)

  • Peng Zhang

    (College of Energy & School of Energy Research, Xiamen University, Xiamen 361102, China)

  • Jinbao Zhao

    (State-Province Joint Engineering Laboratory of Power Source Technology for New Energy Vehicle, State Key Laboratory of Physical Chemistry of Solid Surfaces, Engineering Research Center of Electrochemical Technology, Collaborative Innovation Center of Chemistry for Energy Materials, College of Chemistry and Chemical Engineering, Ministry of Education, Xiamen University, Xiamen 361005, China)

Abstract

Lithium-ion batteries (LIBs) have been widely used in mobile devices, energy storage power stations, medical equipment, and other fields, became an indispensable technological product in modern society. However, the capacity degradation of LIBs limits their long-term deployment, which is not conducive to saving resources. What is more, it will lead to safety problems when the capacity of the battery is degraded. Failure of the battery is a key issue in the research and application of LIBs. Faced with the problem of capacity degradation, various aspects of LIBs have been studied. This paper reviews the electrochemical degradation mechanism of LIBs’ life fade, detection technologies for battery failure, methods to regulate battery capacity degradation, and battery lifetime prognostics. Finally, the development trend and potential challenges of battery capacity degradation research are prospected. All the key insights from this review are expected to advance the research on capacity fading and lifetime prediction techniques for LIBs.

Suggested Citation

  • Wei Li & Hang Li & Zheng He & Weijie Ji & Jing Zeng & Xue Li & Yiyong Zhang & Peng Zhang & Jinbao Zhao, 2022. "Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review," Energies, MDPI, vol. 15(23), pages 1-28, December.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9165-:d:992066
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/23/9165/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/23/9165/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Yuanyuan & Sheng, Hanmin & Cheng, Yuhua & Stroe, Daniel-Ioan & Teodorescu, Remus, 2020. "State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis," Applied Energy, Elsevier, vol. 277(C).
    2. Abdel-Monem, Mohamed & Trad, Khiem & Omar, Noshin & Hegazy, Omar & Van den Bossche, Peter & Van Mierlo, Joeri, 2017. "Influence analysis of static and dynamic fast-charging current profiles on ageing performance of commercial lithium-ion batteries," Energy, Elsevier, vol. 120(C), pages 179-191.
    3. Germana Trentadue & Alexandre Lucas & Marcos Otura & Konstantinos Pliakostathis & Marco Zanni & Harald Scholz, 2018. "Evaluation of Fast Charging Efficiency under Extreme Temperatures," Energies, MDPI, vol. 11(8), pages 1-13, July.
    4. Li Zhang & Min Zheng & Dajun Du & Yihuan Li & Minrui Fei & Yuanjun Guo & Kang Li, 2020. "State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks," Complexity, Hindawi, vol. 2020, pages 1-10, December.
    5. Weili An & Biao Gao & Shixiong Mei & Ben Xiang & Jijiang Fu & Lei Wang & Qiaobao Zhang & Paul K. Chu & Kaifu Huo, 2019. "Scalable synthesis of ant-nest-like bulk porous silicon for high-performance lithium-ion battery anodes," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    6. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
    7. Yu Miao & Patrick Hynan & Annette von Jouanne & Alexandre Yokochi, 2019. "Current Li-Ion Battery Technologies in Electric Vehicles and Opportunities for Advancements," Energies, MDPI, vol. 12(6), pages 1-20, March.
    8. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    9. Wu, Ji & Zhang, Chenbin & Chen, Zonghai, 2016. "An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks," Applied Energy, Elsevier, vol. 173(C), pages 134-140.
    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. Reveles-Miranda, María & Ramirez-Rivera, Victor & Pacheco-Catalán, Daniella, 2024. "Hybrid energy storage: Features, applications, and ancillary benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

    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. 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).
    2. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    3. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    4. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    5. Zuo, Hongyan & Liang, Jingwei & Zhang, Bin & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2023. "Intelligent estimation on state of health of lithium-ion power batteries based on failure feature extraction," Energy, Elsevier, vol. 282(C).
    6. Han, Xiaojuan & Wang, Zuran & Wei, Zixuan, 2021. "A novel approach for health management online-monitoring of lithium-ion batteries based on model-data fusion," Applied Energy, Elsevier, vol. 302(C).
    7. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
    8. Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
    9. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun & Sang-Joon Lee, 2022. "Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
    10. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    11. Ding, Pan & Liu, Xiaojuan & Li, Huiqin & Huang, Zequan & Zhang, Ke & Shao, Long & Abedinia, Oveis, 2021. "Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    12. Gui, Yonghao & Wei, Baoze & Li, Mingshen & Guerrero, Josep M. & Vasquez, Juan C., 2018. "Passivity-based coordinated control for islanded AC microgrid," Applied Energy, Elsevier, vol. 229(C), pages 551-561.
    13. Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
    14. Desreveaux, A. & Bouscayrol, A. & Trigui, R. & Hittinger, E. & Castex, E. & Sirbu, G.M., 2023. "Accurate energy consumption for comparison of climate change impact of thermal and electric vehicles," Energy, Elsevier, vol. 268(C).
    15. Wang, Jiajia & Yue, Xiyan & Wang, Peifen & Yu, Tao & Du, Xiao & Hao, Xiaogang & Abudula, Abuliti & Guan, Guoqing, 2022. "Electrochemical technologies for lithium recovery from liquid resources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    16. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    17. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    18. Anisa Surya Wijareni & Hendri Widiyandari & Agus Purwanto & Aditya Farhan Arif & Mohammad Zaki Mubarok, 2022. "Morphology and Particle Size of a Synthesized NMC 811 Cathode Precursor with Mixed Hydroxide Precipitate and Nickel Sulfate as Nickel Sources and Comparison of Their Electrochemical Performances in an," Energies, MDPI, vol. 15(16), pages 1-15, August.
    19. Alexandru Ciocan & Cosmin Ungureanu & Alin Chitu & Elena Carcadea & George Darie, 2020. "Electrical Longboard for Everyday Urban Commuting," Sustainability, MDPI, vol. 12(19), pages 1-14, September.
    20. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).

    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:15:y:2022:i:23:p:9165-:d:992066. 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.