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

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  • 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.

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  • 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
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    as
    1. 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).
    2. 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).
    3. 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.
    4. 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).
    5. 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).
    6. 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).
    7. 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.
    8. 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).
    9. Li, Mingheng, 2017. "Li-ion dynamics and state of charge estimation," Renewable Energy, Elsevier, vol. 100(C), pages 44-52.
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    14. 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).
    15. 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.
    16. 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).
    17. 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.
    18. 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.
    19. 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).
    20. 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.
    21. 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.
    22. 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).
    23. 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).
    24. 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.
    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. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    27. 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).
    28. 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.
    29. 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).
    30. 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.
    31. 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.
    32. 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).
    33. 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.
    34. 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).
    35. 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.
    36. 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).
    37. 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).
    38. 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).
    39. 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.
    40. 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).
    41. 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).
    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).
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    8. 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.
    9. 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).
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