Prognosticating nonlinear degradation in lithium-ion batteries: operando pressure as an early indicator preceding other signals of capacity fade and safety risks

被引:3
作者
Ding, Shicong [1 ,2 ]
Wang, Li [2 ]
Dai, Haifeng [1 ]
He, Xiangming [2 ]
机构
[1] Tongji Univ, Sch Automot Studies, 4800 Caoan Rd, Shanghai 201804, Peoples R China
[2] Tsinghua Univ, Inst Nucl & New Energy Technol, Beijing 100084, Peoples R China
关键词
Lithium-ion battery; Nonlinear aging; Operando pressure measurements; Early prediction; Evolution of pressure profiles; GRAPHITE; EVOLUTION; ELECTRODE; MODULUS; STATE;
D O I
10.1016/j.ensm.2024.103998
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Lithium-ion batteries occasionally experience sudden drops in capacity, and nonlinear degradation significantly curtails battery lifespan and poses risks to battery safety. However, methods for pinpointing and forecasting the knee-point of nonlinear degradation based solely on electrical signals are not yet timely. In this research, we monitored stress development during extended cycling by conducting precise operando pressure measurements on confined pouch cells. We observed that irreversible cumulative mechanical pressure signals precede the onset of nonlinear battery degradation as indicated by electrical signals. Furthermore, we delved into the mechanism behind pressure signals' ability to foretell the knee-point earlier than electrical signals, which was further substantiated through in-situ and post-mortem analyses. Moreover, we carried out a theoretical dissection to separate the pressure contributions from the anode and cathode, aiming to correlate the pressure profile evolution at the electrode and cell levels with various degradation modes. This proof-of-concept study, spanning the entire battery lifecycle, has shown that pressure signal monitoring can swiftly differentiate between distinct degradation modes. Consequently, this work clears the path for the deployment of simple pressure sensors mounted on the battery surface to diagnose battery degradation pathways.
引用
收藏
页数:14
相关论文
共 82 条
  • [11] Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium-Ion Batteries: A Review
    Ding, Shicong
    Li, Yiding
    Dai, Haifeng
    Wang, Li
    He, Xiangming
    [J]. ADVANCED ENERGY MATERIALS, 2023, 13 (39)
  • [12] Optimizing of Cathode Interface Layers in Organic Solar Cells Using Polyphenols: An Effective Approach
    Ding, Xiaoman
    Lv, Jie
    Liang, Zezhou
    Sun, Xiaokang
    Zhao, Jingjing
    Lu, Manjia
    Wang, Fei
    Zhang, Chenyang
    Zhang, Guangye
    Xu, Tongle
    Hu, Dingqin
    Kan, Zhipeng
    Ruan, Changshun
    Shi, Yumeng
    Lin, Haoran
    Zhang, Wanqing
    Li, Gang
    Hu, Hanlin
    [J]. ADVANCED ENERGY MATERIALS, 2024, 14 (36)
  • [13] Interplay between Elastic and Electrochemical Properties during Active Material Transitions and Aging of a Lithium-Ion Battery
    Feiler, Simon
    Daubinger, Philip
    Gold, Lukas
    Hartmann, Sarah
    Giffin, Guinevere A.
    [J]. BATTERIES & SUPERCAPS, 2023, 6 (04)
  • [14] Fermin-Cueto P., 2020, ENERGY AI, V1, P100006, DOI [10.1016/j.egyai.2020.100006, DOI 10.1016/J.EGYAI.2020.100006]
  • [15] A comparative study of commercial lithium ion battery cycle life in electrical vehicle: Aging mechanism identification
    Han, Xuebing
    Ouyang, Minggao
    Lu, Languang
    Li, Jianqiu
    Zheng, Yuejiu
    Li, Zhe
    [J]. JOURNAL OF POWER SOURCES, 2014, 251 : 38 - 54
  • [16] Onboard early detection and mitigation of lithium plating in fast-charging batteries
    Huang, Wenxiao
    Ye, Yusheng
    Chen, Hao
    Vila, Rafael A.
    Xiang, Andrew
    Wang, Hongxia
    Liu, Fang
    Yu, Zhiao
    Xu, Jinwei
    Zhang, Zewen
    Xu, Rong
    Wu, Yecun
    Chou, Lien-Yang
    Wang, Hansen
    Xu, Junwei
    Boyle, David Tomas
    Li, Yuzhang
    Cui, Yi
    [J]. NATURE COMMUNICATIONS, 2022, 13 (01)
  • [17] Insight-HXMT study of the timing properties of Sco X-1 The Insight-HXMT Collaboration
    Jia, S. M.
    Bu, Q. C.
    Qu, J. L.
    Lu, F. J.
    Zhang, S. N.
    Huang, Y.
    Ma, X.
    Tao, L.
    Xiao, G. C.
    Zhang, W.
    Chen, L.
    Song, L. M.
    Zhang, S.
    Li, T. P.
    Xu, Y. P.
    Cao, X. L.
    Chen, Y.
    Liu, C. Z.
    Cai, C.
    Chang, Z.
    Chen, G.
    Chen, T. X.
    Chen, Y. B.
    Chen, Y. P.
    Cui, W.
    Cui, W. W.
    Deng, J. K.
    Dong, Y. W.
    Du, Y. Y.
    Fu, M. X.
    Gao, G. H.
    Gao, H.
    Gao, M.
    Ge, M. Y.
    Gu, Y. D.
    Guan, J.
    Guo, C. C.
    Han, D. W.
    Huo, J.
    Jiang, L. H.
    Jiang, W. C.
    Jin, J.
    Jin, Y. J.
    Kong, L. D.
    Li, B.
    Li, C. K.
    Li, G.
    Li, M. S.
    Li, W.
    Li, X.
    [J]. JOURNAL OF HIGH ENERGY ASTROPHYSICS, 2020, 25 : 1 - 9
  • [18] Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique
    Jiang, Bo
    Tao, Siyi
    Wang, Xueyuan
    Zhu, Jiangong
    Wei, Xuezhe
    Dai, Haifeng
    [J]. ENERGY, 2023, 278
  • [19] Inhibiting Solvent Co-Intercalation in a Graphite Anode by a Localized High-Concentration Electrolyte in Fast-Charging Batteries
    Jiang, Li-Li
    Yan, Chong
    Yao, Yu-Xing
    Cai, Wenlong
    Huang, Jia-Qi
    Zhang, Qiang
    [J]. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 2021, 60 (07) : 3402 - 3406
  • [20] G-K curve-based knee point prediction method for Li-ion batteries
    Kim, Kwangrae
    Kim, Minho
    Churr, Huiyong
    Lee, Gyeonghwan
    Han, Soohee
    [J]. 2021 21ST INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2021), 2021, : 1190 - 1193