IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v376y2024ipas030626192401554x.html
   My bibliography  Save this article

Predict the lifetime of lithium-ion batteries using early cycles: A review

Author

Listed:
  • Yang, Minxing
  • Sun, Xiaofei
  • Liu, Rui
  • Wang, Lingzhi
  • Zhao, Fei
  • Mei, Xuesong

Abstract

With the rapid development of lithium-ion batteries in recent years, predicting their remaining useful life based on the early stages of cycling has become increasingly important. Accurate life prediction using early cycles (e.g., first several cycles) is crucial to rational design, optimal production, efficient management, and safe usage of advanced batteries in energy storage applications such as portable electronics, electric vehicles, and smart grids. In this review, the necessity and urgency of early-stage prediction of battery life are highlighted by systematically analyzing the primary aging mechanisms of lithium-ion batteries, and the latest fast progress on early-stage prediction is then comprehensively outlined into mechanism-guided, experience-based, data-driven, and fusion-combined approaches. The key models of each method and their typical research works are profoundly analyzed, and the pros and cons of each method are then evolved with an in-depth comparison of their prediction performances. The current challenges and future perspectives of early-stage prediction are finally addressed. This review is advantageous in fully and briefly understanding the principles, methods, development, and application of early-stage prediction of battery life and is directed to expedite research on novel, accurate, efficient, and simple theories and technologies for early-stage prediction.

Suggested Citation

  • Yang, Minxing & Sun, Xiaofei & Liu, Rui & Wang, Lingzhi & Zhao, Fei & Mei, Xuesong, 2024. "Predict the lifetime of lithium-ion batteries using early cycles: A review," Applied Energy, Elsevier, vol. 376(PA).
    • Handle: RePEc:eee:appene:v:376:y:2024:i:pa:s030626192401554x
    DOI: 10.1016/j.apenergy.2024.124171
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626192401554X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124171?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "Parallel State Fusion LSTM-based Early-cycle Stage Lithium-ion Battery RUL Prediction Under Lebesgue Sampling Framework," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    2. Zhang, Chen & Wang, Hongmin & Wu, Lifeng, 2023. "Life prediction model for lithium-ion battery considering fast-charging protocol," Energy, Elsevier, vol. 263(PE).
    3. Zhang, Yongzhi & Xiong, Rui & He, Hongwen & Qu, Xiaobo & Pecht, Michael, 2019. "State of charge-dependent aging mechanisms in graphite/Li(NiCoAl)O2 cells: Capacity loss modeling and remaining useful life prediction," Applied Energy, Elsevier, vol. 255(C).
    4. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    5. Yan, Lisen & Peng, Jun & Gao, Dianzhu & Wu, Yue & Liu, Yongjie & Li, Heng & Liu, Weirong & Huang, Zhiwu, 2022. "A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery," Energy, Elsevier, vol. 243(C).
    6. Meng, Fanbing & Yang, Fangfang & Yang, Jun & Xie, Min, 2023. "A power model considering initial battery state for remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Aging abnormality detection of lithium-ion batteries combining feature engineering and deep learning," Energy, Elsevier, vol. 297(C).
    8. Yunwei Zhang & Qiaochu Tang & Yao Zhang & Jiabin Wang & Ulrich Stimming & Alpha A. Lee, 2020. "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    9. 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).
    10. Wang, Cong & Chen, Yunxia & Zhang, Qingyuan & Zhu, Jiaxiao, 2023. "Dynamic early recognition of abnormal lithium-ion batteries before capacity drops using self-adaptive quantum clustering," Applied Energy, Elsevier, vol. 336(C).
    11. Pan, Yue & Kong, Xiangdong & Yuan, Yuebo & Sun, Yukun & Han, Xuebing & Yang, Hongxin & Zhang, Jianbiao & Liu, Xiaoan & Gao, Panlong & Li, Yihui & Lu, Languang & Ouyang, Minggao, 2023. "Detecting the foreign matter defect in lithium-ion batteries based on battery pilot manufacturing line data analyses," Energy, Elsevier, vol. 262(PB).
    12. Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
    13. Xin Lai & Ming Yuan & Xiaopeng Tang & Yi Yao & Jiahui Weng & Furong Gao & Weiguo Ma & Yuejiu Zheng, 2022. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing," Energies, MDPI, vol. 15(19), pages 1-20, October.
    14. Calum Strange & Rasheed Ibraheem & Gonçalo dos Reis, 2023. "Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling," Energies, MDPI, vol. 16(7), pages 1-14, April.
    15. 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).
    16. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
    17. Chen, Dinghong & Zhang, Weige & Zhang, Caiping & Sun, Bingxiang & Cong, XinWei & Wei, Shaoyuan & Jiang, Jiuchun, 2022. "A novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles," Applied Energy, Elsevier, vol. 327(C).
    18. Kim, Minho & Chun, Huiyong & Kim, Jungsoo & Kim, Kwangrae & Yu, Jungwook & Kim, Taegyun & Han, Soohee, 2019. "Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search," Applied Energy, Elsevier, vol. 254(C).
    19. Hong, Joonki & Lee, Dongheon & Jeong, Eui-Rim & Yi, Yung, 2020. "Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning," Applied Energy, Elsevier, vol. 278(C).
    20. He, Ning & Wang, Qiqi & Lu, Zhenfeng & Chai, Yike & Yang, Fangfang, 2024. "Early prediction of battery lifetime based on graphical features and convolutional neural networks," Applied Energy, Elsevier, vol. 353(PA).
    21. Peter M. Attia & Aditya Grover & Norman Jin & Kristen A. Severson & Todor M. Markov & Yang-Hung Liao & Michael H. Chen & Bryan Cheong & Nicholas Perkins & Zi Yang & Patrick K. Herring & Muratahan Ayko, 2020. "Closed-loop optimization of fast-charging protocols for batteries with machine learning," Nature, Nature, vol. 578(7795), pages 397-402, February.
    22. Thelen, Adam & Li, Meng & Hu, Chao & Bekyarova, Elena & Kalinin, Sergey & Sanghadasa, Mohan, 2022. "Augmented model-based framework for battery remaining useful life prediction," Applied Energy, Elsevier, vol. 324(C).
    23. Ibraheem, Rasheed & Wu, Yue & Lyons, Terry & dos Reis, Gonçalo, 2023. "Early prediction of Lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates," Applied Energy, Elsevier, vol. 352(C).
    24. Jiahuan Lu & Rui Xiong & Jinpeng Tian & Chenxu Wang & Fengchun Sun, 2023. "Deep learning to estimate lithium-ion battery state of health without additional degradation experiments," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    25. Chen, Zhang & Chen, Liqun & Ma, Zhengwei & Xu, Kangkang & Zhou, Yu & Shen, Wenjing, 2023. "Joint modeling for early predictions of Li-ion battery cycle life and degradation trajectory," Energy, Elsevier, vol. 277(C).
    26. Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).
    27. Tang, Aihua & Jiang, Yihan & Nie, Yuwei & Yu, Quanqing & Shen, Weixiang & Pecht, Michael G., 2023. "Health and lifespan prediction considering degradation patterns of lithium-ion batteries based on transferable attention neural network," Energy, Elsevier, vol. 279(C).
    28. Yang, Fangfang & Song, Xiangbao & Dong, Guangzhong & Tsui, Kwok-Leung, 2019. "A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries," Energy, Elsevier, vol. 171(C), pages 1173-1182.
    29. Zhang, Yu & Peng, Zhen & Guan, Yong & Wu, Lifeng, 2021. "Prognostics of battery cycle life in the early-cycle stage based on hybrid model," Energy, Elsevier, vol. 221(C).
    30. Kim, Jaewon & Sin, Seunghwa & Kim, Jonghoon, 2024. "Early remaining-useful-life prediction applying discrete wavelet transform combined with improved semi-empirical model for high-fidelity in battery energy storage system," Energy, Elsevier, vol. 297(C).
    Full references (including those not matched with items on IDEAS)

    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. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
    2. Jiang, Lidang & Li, Zhuoxiang & Hu, Changyan & Chen, Junxiong & Huang, Qingsong & He, Ge, 2024. "A robust adapted Flexible Parallel Neural Network architecture for early prediction of lithium battery lifespan," Energy, Elsevier, vol. 308(C).
    3. Ibraheem, Rasheed & Dechent, Philipp & dos Reis, Gonçalo, 2025. "Path signature-based life prognostics of Li-ion battery using pulse test data," Applied Energy, Elsevier, vol. 378(PA).
    4. Zhang, Chen & Wang, Hongmin & Wu, Lifeng, 2023. "Life prediction model for lithium-ion battery considering fast-charging protocol," Energy, Elsevier, vol. 263(PE).
    5. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Two-stage prediction method for capacity aging trajectories of lithium-ion batteries based on Siamese-convolutional neural network," Energy, Elsevier, vol. 295(C).
    6. He, Ning & Wang, Qiqi & Lu, Zhenfeng & Chai, Yike & Yang, Fangfang, 2024. "Early prediction of battery lifetime based on graphical features and convolutional neural networks," Applied Energy, Elsevier, vol. 353(PA).
    7. Zhao, Wanjie & Ding, Wei & Zhang, Shujing & Zhang, Zhen, 2024. "Enhancing lithium-ion battery lifespan early prediction using a multi-branch vision transformer model," Energy, Elsevier, vol. 302(C).
    8. Song, Dengwei & Cheng, Yujie & Zhou, An & Lu, Chen & Chong, Jin & Ma, Jian, 2024. "Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    9. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    10. Zhai, Qiangxiang & Jiang, Hongmin & Long, Nengbing & Kang, Qiaoling & Meng, Xianhe & Zhou, Mingjiong & Yan, Lijing & Ma, Tingli, 2024. "Machine learning for full lifecycle management of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    11. 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).
    12. Matthieu Dubarry & David Beck, 2021. "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis," Energies, MDPI, vol. 14(9), pages 1-24, April.
    13. Che, Yunhong & Zheng, Yusheng & Wu, Yue & Sui, Xin & Bharadwaj, Pallavi & Stroe, Daniel-Ioan & Yang, Yalian & Hu, Xiaosong & Teodorescu, Remus, 2022. "Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network," Applied Energy, Elsevier, vol. 323(C).
    14. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    15. Chen, Zhang & Chen, Liqun & Ma, Zhengwei & Xu, Kangkang & Zhou, Yu & Shen, Wenjing, 2023. "Joint modeling for early predictions of Li-ion battery cycle life and degradation trajectory," Energy, Elsevier, vol. 277(C).
    16. Zhao, Guangcai & Kang, Yongzhe & Huang, Peng & Duan, Bin & Zhang, Chenghui, 2023. "Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning," Energy, Elsevier, vol. 282(C).
    17. Liu, Yunpeng & Hou, Bo & Ahmed, Moin & Mao, Zhiyu & Feng, Jiangtao & Chen, Zhongwei, 2024. "A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments," Applied Energy, Elsevier, vol. 358(C).
    18. Wang, Cong & Chen, Yunxia, 2024. "Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
    19. Zhang, Meng & Hu, Tao & Wu, Lifeng & Kang, Guoqing & Guan, Yong, 2021. "A method for capacity estimation of lithium-ion batteries based on adaptive time-shifting broad learning system," Energy, Elsevier, vol. 231(C).
    20. Chen, Dinghong & Zhang, Weige & Zhang, Caiping & Sun, Bingxiang & Cong, XinWei & Wei, Shaoyuan & Jiang, Jiuchun, 2022. "A novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles," Applied Energy, Elsevier, vol. 327(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:eee:appene:v:376:y:2024:i:pa:s030626192401554x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.