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A review on structuralized current collectors for high-performance lithium-ion battery anodes

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  • Yang, Yang
  • Yuan, Wei
  • Zhang, Xiaoqing
  • Ke, Yuzhi
  • Qiu, Zhiqiang
  • Luo, Jian
  • Tang, Yong
  • Wang, Chun
  • Yuan, Yuhang
  • Huang, Yao

Abstract

As environmentally friendly and high-energy density rechargeable energy storage devices, lithium-ion batteries (LIBs) have thriving prospects in the field of energy. The current collector, which serves as an important component of LIBs, significantly influences the electrochemical performance of the battery. Numerous efforts have been spent on the design and fabrication of high-performance negative current collectors in the field of LIBs to achieve excellent battery performances. These high-performance current collectors are mostly structuralized to achieve special functions. Hence, different types of structuralized current collectors used for LIB anodes are comprehensively discussed and summarized in this paper. The structuralized current collectors used in LIB anodes are classified into planar-plate-based special-surface current collectors and the 3D framework-based porous current collectors. Both types of the structuralized current collectors are further divided into the single-component and multicomponent current collectors. More subsections are provided and focus on providing description of the developing strategies, fabrication methods, electrochemical behaviors, in-depth reasons for high performances, and advantages and challenges for real applications of the structuralized current collectors in detail. Subsequently, the challenges and future research directions of structuralized anode current collectors are reasonably clarified. Based on an objective summary of the high-performance negative current collectors, this review provides an enlightening guide for the future development of current collectors and LIBs. The fundamental conclusions can also be extended to other energy storage devices.

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  • Yang, Yang & Yuan, Wei & Zhang, Xiaoqing & Ke, Yuzhi & Qiu, Zhiqiang & Luo, Jian & Tang, Yong & Wang, Chun & Yuan, Yuhang & Huang, Yao, 2020. "A review on structuralized current collectors for high-performance lithium-ion battery anodes," Applied Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920309764
    DOI: 10.1016/j.apenergy.2020.115464
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    as
    1. Rechkemmer, Sabrina Kathrin & Zang, Xiaoyun & Zhang, Weimin & Sawodny, Oliver, 2019. "Lifetime optimized charging strategy of Li-ion cells based on daily driving cycle of electric two-wheelers," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. de Hoog, Joris & Timmermans, Jean-Marc & Ioan-Stroe, Daniel & Swierczynski, Maciej & Jaguemont, Joris & Goutam, Shovon & Omar, Noshin & Van Mierlo, Joeri & Van Den Bossche, Peter, 2017. "Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation," Applied Energy, Elsevier, vol. 200(C), pages 47-61.
    3. Zhai, Pei & Isaacs, Jacqueline A. & Eckelman, Matthew J., 2016. "Net energy benefits of carbon nanotube applications," Applied Energy, Elsevier, vol. 173(C), pages 624-634.
    4. Lee, Min Seok & Choi, Hyun-Jung & Baek, Jong-Beom & Chang, Dong Wook, 2017. "Simple solution-based synthesis of pyridinic-rich nitrogen-doped graphene nanoplatelets for supercapacitors," Applied Energy, Elsevier, vol. 195(C), pages 1071-1078.
    5. Li, Min & Mu, Boyuan, 2019. "Effect of different dimensional carbon materials on the properties and application of phase change materials: A review," Applied Energy, Elsevier, vol. 242(C), pages 695-715.
    6. Shi, Yu & Eze, Chika & Xiong, Binyu & He, Weidong & Zhang, Han & Lim, T.M. & Ukil, A. & Zhao, Jiyun, 2019. "Recent development of membrane for vanadium redox flow battery applications: A review," Applied Energy, Elsevier, vol. 238(C), pages 202-224.
    7. Zhang, Caiping & Wang, Yubin & Gao, Yang & Wang, Fang & Mu, Biqiang & Zhang, Weige, 2019. "Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method," Applied Energy, Elsevier, vol. 256(C).
    8. Li, Ang & Wang, Jingjing & Dong, Cheng & Dong, Wenjun & Atinafu, Dimberu G. & Chen, Xiao & Gao, Hongyi & Wang, Ge, 2018. "Core-sheath structural carbon materials for integrated enhancement of thermal conductivity and capacity," Applied Energy, Elsevier, vol. 217(C), pages 369-376.
    9. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    10. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    11. Wang, Shaofeng & Zhu, Yanping & Xu, Xiaomin & Sunarso, Jaka & Shao, Zongping, 2017. "Adsorption-based synthesis of Co3O4/C composite anode for high performance lithium-ion batteries," Energy, Elsevier, vol. 125(C), pages 569-575.
    12. David, Lamuel & Ruther, Rose E. & Mohanty, Debasish & Meyer, Harry M. & Sheng, Yangping & Kalnaus, Sergiy & Daniel, Claus & Wood, David L., 2018. "Identifying degradation mechanisms in lithium-ion batteries with coating defects at the cathode," Applied Energy, Elsevier, vol. 231(C), pages 446-455.
    13. Yang, Yang & Yuan, Wei & Zhang, Xiaoqing & Yuan, Yuhang & Wang, Chun & Ye, Yintong & Huang, Yao & Qiu, Zhiqiang & Tang, Yong, 2020. "Overview on the applications of three-dimensional printing for rechargeable lithium-ion batteries," Applied Energy, Elsevier, vol. 257(C).
    14. Frattini, Domenico & Accardo, Grazia & Duarte, Kimberley D.Z. & Kim, Do-Heyoung & Kwon, Yongchai, 2020. "Improved biofilm adhesion and electrochemical properties of a graphite-cement composite with silica nanoflowers versus two benchmark carbon felts," Applied Energy, Elsevier, vol. 261(C).
    15. Wang, Mingyue & Huang, Ying & Wang, Ke & Zhu, Yade & Zhang, Na & Zhang, Hongming & Li, Suping & Feng, Zhenhe, 2018. "PVD synthesis of binder-free silicon and carbon coated 3D α-Fe2O3 nanorods hybrid films as high-capacity and long-life anode for flexible lithium-ion batteries," Energy, Elsevier, vol. 164(C), pages 1021-1029.
    16. Sturm, J. & Ennifar, H. & Erhard, S.V. & Rheinfeld, A. & Kosch, S. & Jossen, A., 2018. "State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter," Applied Energy, Elsevier, vol. 223(C), pages 103-123.
    17. Uebbing, Jennifer & Rihko-Struckmann, Liisa K. & Sundmacher, Kai, 2019. "Exergetic assessment of CO2 methanation processes for the chemical storage of renewable energies," Applied Energy, Elsevier, vol. 233, pages 271-282.
    18. Chen, Renjie & Huang, Xinyu & Deng, Weibin & Zheng, Ruizhi & Aftab, Waseem & Shi, Jinmin & Xie, Delong & Zou, Ruqiang & Mei, Yi, 2020. "Facile preparation of flexible eicosane/SWCNTs phase change films via colloid aggregation for thermal energy storage," Applied Energy, Elsevier, vol. 260(C).
    19. Chun-Peng Yang & Ya-Xia Yin & Shuai-Feng Zhang & Nian-Wu Li & Yu-Guo Guo, 2015. "Accommodating lithium into 3D current collectors with a submicron skeleton towards long-life lithium metal anodes," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    20. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    21. Du, Jiuyu & Liu, Ye & Mo, Xinying & Li, Yalun & Li, Jianqiu & Wu, Xiaogang & Ouyang, Minggao, 2019. "Impact of high-power charging on the durability and safety of lithium batteries used in long-range battery electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    22. Lv, Jie & Lin, Shili & Song, Wenji & Chen, Mingbiao & Feng, Ziping & Li, Yongliang & Ding, Yulong, 2019. "Performance of LiFePO4 batteries in parallel based on connection topology," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    23. Yuan, Wei & Tang, Yong & Yang, Xiaojun & Wan, Zhenping, 2012. "Porous metal materials for polymer electrolyte membrane fuel cells – A review," Applied Energy, Elsevier, vol. 94(C), pages 309-329.
    24. Tan, Weng Cheong & Saw, Lip Huat & Thiam, Hui San & Xuan, Jin & Cai, Zuansi & Yew, Ming Chian, 2018. "Overview of porous media/metal foam application in fuel cells and solar power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 181-197.
    25. Ruan, Haijun & Jiang, Jiuchun & Sun, Bingxiang & Su, Xiaojia & He, Xitian & Zhao, Kejie, 2019. "An optimal internal-heating strategy for lithium-ion batteries at low temperature considering both heating time and lifetime reduction," Applied Energy, Elsevier, vol. 256(C).
    26. Killer, Marvin & Farrokhseresht, Mana & Paterakis, Nikolaos G., 2020. "Implementation of large-scale Li-ion battery energy storage systems within the EMEA region," Applied Energy, Elsevier, vol. 260(C).
    27. Lyu, Chao & Song, Yankong & Zheng, Jun & Luo, Weilin & Hinds, Gareth & Li, Junfu & Wang, Lixin, 2019. "In situ monitoring of lithium-ion battery degradation using an electrochemical model," Applied Energy, Elsevier, vol. 250(C), pages 685-696.
    28. Yang, Ruixin & Xiong, Rui & Ma, Suxiao & Lin, Xinfan, 2020. "Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks," Applied Energy, Elsevier, vol. 260(C).
    29. Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
    30. Duong, Van-Huan & Bastawrous, Hany Ayad & See, Khay Wai, 2017. "Accurate approach to the temperature effect on state of charge estimation in the LiFePO4 battery under dynamic load operation," Applied Energy, Elsevier, vol. 204(C), pages 560-571.
    31. Gandoman, Foad H. & Jaguemont, Joris & Goutam, Shovon & Gopalakrishnan, Rahul & Firouz, Yousef & Kalogiannis, Theodoros & Omar, Noshin & Van Mierlo, Joeri, 2019. "Concept of reliability and safety assessment of lithium-ion batteries in electric vehicles: Basics, progress, and challenges," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
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