IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v241y2022ics0360544221031303.html
   My bibliography  Save this article

A review on second-life of Li-ion batteries: prospects, challenges, and issues

Author

Listed:
  • Shahjalal, Mohammad
  • Roy, Probir Kumar
  • Shams, Tamanna
  • Fly, Ashley
  • Chowdhury, Jahedul Islam
  • Ahmed, Md. Rishad
  • Liu, Kailong

Abstract

High energy density has made Li-ion battery become a reliable energy storage technology for transport-grid applications. Safely disposing batteries that below 80% of their nominal capacity is a matter of great concern to reduce overall carbon footprint. As battery typically accounts for 40% of the total cost of an electrical vehicle, it becomes necessary to combine reutilization and recycling for extending the lifetime of retired automotive batteries and make the overall battery supply chain profitable rather than dumping them directly. From an economic, technical, and environmental standpoint, this paper provides a comprehensive overview of the present state of second-life Li-ion batteries through exploring relevant literature. Specifically, the fundamental of Li-ion battery degradation and experimental approaches are first surveyed. After examining the obstacles and methods of reusing and recycling Li-ion battery, related applications, cost issues, and business models of second-life Li-ion batteries are discussed. By offering a systematical survey of current status of recycled Li-ion battery, this review could inform commercial technology selections and academic research agendas alike, thus boosting progress in Li-ion battery second-life applications.

Suggested Citation

  • Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
  • Handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221031303
    DOI: 10.1016/j.energy.2021.122881
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221031303
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.122881?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. 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.
    2. Chen, Lin & Wang, Huimin & Liu, Bohao & Wang, Yijue & Ding, Yunhui & Pan, Haihong, 2021. "Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation," Energy, Elsevier, vol. 215(PA).
    3. Vichard, L. & Ravey, A. & Venet, P. & Harel, F. & Pelissier, S. & Hissel, D., 2021. "A method to estimate battery SOH indicators based on vehicle operating data only," Energy, Elsevier, vol. 225(C).
    4. Tong, Shi Jie & Same, Adam & Kootstra, Mark A. & Park, Jae Wan, 2013. "Off-grid photovoltaic vehicle charge using second life lithium batteries: An experimental and numerical investigation," Applied Energy, Elsevier, vol. 104(C), pages 740-750.
    5. Chen, Tao & Cai, Liang & Wen, Xiantai & Zhang, Xiaosong, 2021. "Experimental research and energy consumption analysis on the economic performance of a hybrid-power gas engine heat pump with LiFePO4 battery," Energy, Elsevier, vol. 214(C).
    6. 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).
    7. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    8. Park, Jinhyeong & Kim, Kunwoo & Park, Seongyun & Baek, Jongbok & Kim, Jonghoon, 2021. "Complementary cooperative SOC/capacity estimator based on the discrete variational derivative combined with the DEKF for electric power applications," Energy, Elsevier, vol. 232(C).
    9. Caiping Zhang & Jiuchun Jiang & Linjing Zhang & Sijia Liu & Leyi Wang & Poh Chiang Loh, 2016. "A Generalized SOC-OCV Model for Lithium-Ion Batteries and the SOC Estimation for LNMCO Battery," Energies, MDPI, vol. 9(11), pages 1-16, November.
    10. 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.
    11. 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).
    12. Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).
    13. Wei He & Michael Pecht & David Flynn & Fateme Dinmohammadi, 2018. "A Physics-Based Electrochemical Model for Lithium-Ion Battery State-of-Charge Estimation Solved by an Optimised Projection-Based Method and Moving-Window Filtering," Energies, MDPI, vol. 11(8), pages 1-23, August.
    14. Richa, Kirti & Babbitt, Callie W. & Gaustad, Gabrielle & Wang, Xue, 2014. "A future perspective on lithium-ion battery waste flows from electric vehicles," Resources, Conservation & Recycling, Elsevier, vol. 83(C), pages 63-76.
    15. Šeruga, Domen & Gosar, Aleš & Sweeney, Caoimhe A. & Jaguemont, Joris & Van Mierlo, Joeri & Nagode, Marko, 2021. "Continuous modelling of cyclic ageing for lithium-ion batteries," Energy, Elsevier, vol. 215(PB).
    16. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    17. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    18. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    19. Yu, Jianbo, 2018. "State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 82-95.
    20. Jiang, Lulu & Deng, Zhongwei & Tang, Xiaolin & Hu, Lin & Lin, Xianke & Hu, Xiaosong, 2021. "Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data," Energy, Elsevier, vol. 234(C).
    21. Lai, Xin & Yi, Wei & Cui, Yifan & Qin, Chao & Han, Xuebing & Sun, Tao & Zhou, Long & Zheng, Yuejiu, 2021. "Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter," Energy, Elsevier, vol. 216(C).
    22. Mathews, Ian & Xu, Bolun & He, Wei & Barreto, Vanessa & Buonassisi, Tonio & Peters, Ian Marius, 2020. "Technoeconomic model of second-life batteries for utility-scale solar considering calendar and cycle aging," Applied Energy, Elsevier, vol. 269(C).
    23. 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.
    24. Comello, Stephen & Glenk, Gunther & Reichelstein, Stefan, 2021. "Transitioning to clean energy transportation services: Life-cycle cost analysis for vehicle fleets," Applied Energy, Elsevier, vol. 285(C).
    25. 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).
    26. Tian, Jiaqiang & Xu, Ruilong & Wang, Yujie & Chen, Zonghai, 2021. "Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries," Energy, Elsevier, vol. 221(C).
    27. Galeotti, Matteo & Cinà, Lucio & Giammanco, Corrado & Cordiner, Stefano & Di Carlo, Aldo, 2015. "Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy," Energy, Elsevier, vol. 89(C), pages 678-686.
    28. Hashemi, Seyed Reza & Mahajan, Ajay Mohan & Farhad, Siamak, 2021. "Online estimation of battery model parameters and state of health in electric and hybrid aircraft application," Energy, Elsevier, vol. 229(C).
    29. Zhou, Di & Zheng, Wenbin & Chen, Shaohui & Fu, Ping & Zhu, Hongyu & Song, Bai & Qu, Xisong & Wang, Tiancheng, 2021. "Research on state of health prediction model for lithium batteries based on actual diverse data," Energy, Elsevier, vol. 230(C).
    30. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    31. Li, Mingheng, 2017. "Li-ion dynamics and state of charge estimation," Renewable Energy, Elsevier, vol. 100(C), pages 44-52.
    32. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    33. Martinez-Laserna, E. & Gandiaga, I. & Sarasketa-Zabala, E. & Badeda, J. & Stroe, D.-I. & Swierczynski, M. & Goikoetxea, A., 2018. "Battery second life: Hype, hope or reality? A critical review of the state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 701-718.
    34. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
    35. Jiang, Cong & Wang, Shunli & Wu, Bin & Fernandez, Carlos & Xiong, Xin & Coffie-Ken, James, 2021. "A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter," Energy, Elsevier, vol. 219(C).
    36. 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.
    37. Gavin Harper & Roberto Sommerville & Emma Kendrick & Laura Driscoll & Peter Slater & Rustam Stolkin & Allan Walton & Paul Christensen & Oliver Heidrich & Simon Lambert & Andrew Abbott & Karl Ryder & L, 2019. "Recycling lithium-ion batteries from electric vehicles," Nature, Nature, vol. 575(7781), pages 75-86, November.
    38. 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).
    39. 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).
    40. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    41. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
    42. Lybbert, M. & Ghaemi, Z. & Balaji, A.K. & Warren, R., 2021. "Integrating life cycle assessment and electrochemical modeling to study the effects of cell design and operating conditions on the environmental impacts of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    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. Zhang, Shuxin & Liu, Zhitao & Su, Hongye, 2023. "State of health estimation for lithium-ion batteries on few-shot learning," Energy, Elsevier, vol. 268(C).
    2. Chirumalla, Koteshwar & Kulkov, Ignat & Parida, Vinit & Dahlquist, Erik & Johansson, Glenn & Stefan, Ioana, 2024. "Enabling battery circularity: Unlocking circular business model archetypes and collaboration forms in the electric vehicle battery ecosystem," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    3. Lander, Laura & Tagnon, Chris & Nguyen-Tien, Viet & Kendrick, Emma & Elliott, Robert J.R. & Abbott, Andrew P. & Edge, Jacqueline S. & Offer, Gregory J., 2023. "Breaking it down: A techno-economic assessment of the impact of battery pack design on disassembly costs," Applied Energy, Elsevier, vol. 331(C).
    4. Terkes, Musa & Arikan, Oktay & Gokalp, Erdin, 2024. "The effect of electric vehicle charging demand variability on optimal hybrid power systems with second-life lithium-ion or fresh Na–S batteries considering power quality," Energy, Elsevier, vol. 288(C).
    5. Guo, Fei & Wu, Xiongwei & Liu, Lili & Ye, Jilei & Wang, Tao & Fu, Lijun & Wu, Yuping, 2023. "Prediction of remaining useful life and state of health of lithium batteries based on time series feature and Savitzky-Golay filter combined with gated recurrent unit neural network," Energy, Elsevier, vol. 270(C).
    6. Dan, Zhaohui & Song, Aoye & Yu, Xiaojun & Zhou, Yuekuan, 2024. "Electrification-driven circular economy with machine learning-based multi-scale and cross-scale modelling approach," Energy, Elsevier, vol. 299(C).
    7. 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).
    8. Gu, Pingwei & Zhang, Ying & Duan, Bin & Zhang, Chenghui & Kang, Yongzhe, 2024. "Rapid and flexible lithium-ion battery performance evaluation using random charging curve based on deep learning," Energy, Elsevier, vol. 293(C).
    9. Anne Christine Lusk & Xin Li & Qiming Liu, 2023. "If the Government Pays for Full Home-Charger Installation, Would Affordable-Housing and Middle-Income Residents Buy Electric Vehicles?," Sustainability, MDPI, vol. 15(5), pages 1-26, March.
    10. 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).
    11. Rajamani, Arunkumar & Panneerselvam, Thamayanthi & Murugan, Ramaswamy & Ramaswamy, Arun Prasath, 2023. "Electrospun derived polymer-garnet composite quasi solid state electrolyte with low interface resistance for lithium metal batteries," Energy, Elsevier, vol. 263(PE).
    12. Zeng, Jing & Liu, Sifeng, 2023. "Forecasting the sustainable classified recycling of used lithium batteries by gray Graphical Evaluation and Review Technique," Renewable Energy, Elsevier, vol. 202(C), pages 602-612.
    13. Kim, Kyunghyun & Choi, Jung-Il, 2023. "Effect of cell-to-cell variation and module configuration on the performance of lithium-ion battery systems," Applied Energy, Elsevier, vol. 352(C).
    14. Xia, Xiaoning & Li, Pengwei & Cheng, Yang, 2024. "Economic and environmental evaluation of different collection models for spent power batteries," Energy, Elsevier, vol. 299(C).
    15. Tang, Hong & Wang, Shengwei, 2023. "Life-cycle economic analysis of thermal energy storage, new and second-life batteries in buildings for providing multiple flexibility services in electricity markets," Energy, Elsevier, vol. 264(C).
    16. Harper, Gavin D.J. & Kendrick, Emma & Anderson, Paul A. & Mrozik, Wojciech & Christensen, Paul & Lambert, Simon & Greenwood, David & Das, Prodip K. & Ahmeid, Mohamed & Milojevic, Zoran & Du, Wenjia & , 2023. "Roadmap for a sustainable circular economy in lithium-ion and future battery technologies," LSE Research Online Documents on Economics 118420, London School of Economics and Political Science, LSE Library.
    17. Abdelkareem, Mohammad Ali & Abbas, Qaisar & Sayed, Enas Taha & Shehata, N. & Parambath, J.B.M. & Alami, Abdul Hai & Olabi, A.G., 2024. "Recent advances on metal-organic frameworks (MOFs) and their applications in energy conversion devices: Comprehensive review," Energy, Elsevier, vol. 299(C).
    18. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    19. Wu, Jiafeng & Li, Lin & Yin, Zichao & Li, Zhe & Wang, Tong & Tan, Yunfeng & Tan, Dapeng, 2024. "Mass transfer mechanism of multiphase shear flows and interphase optimization solving method," Energy, Elsevier, vol. 292(C).
    20. Ma, Qianli & Wei, Wei & Mei, Shengwei, 2024. "Health-aware coordinate long-term and short-term operation for BESS in energy and frequency regulation markets," Applied Energy, Elsevier, vol. 356(C).
    21. Sai Vinayak Ganesh & Matilde D’Arpino, 2023. "Critical Comparison of Li-Ion Aging Models for Second Life Battery Applications," Energies, MDPI, vol. 16(7), pages 1-23, March.

    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. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
    2. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    3. 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).
    4. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    5. Chen, Zhang & Shen, Wenjing & Chen, Liqun & Wang, Shuqiang, 2022. "Adaptive online capacity prediction based on transfer learning for fast charging lithium-ion batteries," Energy, Elsevier, vol. 248(C).
    6. 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).
    7. Shen, Jiangwei & Ma, Wensai & Shu, Xing & Shen, Shiquan & Chen, Zheng & Liu, Yonggang, 2023. "Accurate state of health estimation for lithium-ion batteries under random charging scenarios," Energy, Elsevier, vol. 279(C).
    8. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    9. Khaleghi, Sahar & Hosen, Md Sazzad & Karimi, Danial & Behi, Hamidreza & Beheshti, S. Hamidreza & Van Mierlo, Joeri & Berecibar, Maitane, 2022. "Developing an online data-driven approach for prognostics and health management of lithium-ion batteries," Applied Energy, Elsevier, vol. 308(C).
    10. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
    11. 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).
    12. Li, Renzheng & Hong, Jichao & Zhang, Huaqin & Chen, Xinbo, 2022. "Data-driven battery state of health estimation based on interval capacity for real-world electric vehicles," Energy, Elsevier, vol. 257(C).
    13. Liu, Gengfeng & Zhang, Xiangwen & Liu, Zhiming, 2022. "State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm," Energy, Elsevier, vol. 259(C).
    14. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    15. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
    16. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    17. Lin, Yan-Hui & Ruan, Sheng-Jia & Chen, Yun-Xia & Li, Yan-Fu, 2023. "Physics-informed deep learning for lithium-ion battery diagnostics using electrochemical impedance spectroscopy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    18. 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).
    19. Yang, Bo & Qian, Yucun & Li, Qiang & Chen, Qian & Wu, Jiyang & Luo, Enbo & Xie, Rui & Zheng, Ruyi & Yan, Yunfeng & Su, Shi & Wang, Jingbo, 2024. "Critical summary and perspectives on state-of-health of lithium-ion battery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    20. Braco, Elisa & San Martín, Idoia & Sanchis, Pablo & Ursúa, Alfredo & Stroe, Daniel-Ioan, 2022. "State of health estimation of second-life lithium-ion batteries under real profile operation," 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:eee:energy:v:241:y:2022:i:c:s0360544221031303. 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.journals.elsevier.com/energy .

    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.