IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v171y2019icp1217-1228.html
   My bibliography  Save this article

Investigation of the temperature dependence of lithium plating onset conditions in commercial Li-ion batteries

Author

Listed:
  • Angeles Cabañero, Maria
  • Altmann, Johannes
  • Gold, Lukas
  • Boaretto, Nicola
  • Müller, Jana
  • Hein, Simon
  • Zausch, Jochen
  • Kallo, Josef
  • Latz, Arnulf

Abstract

Fast charging is one of the main challenges in Lithium-ion battery applications. Especially at low temperatures and high C-rates, capacity loss due to lithium plating is identified as the main aging effect. Electrochemical models are able to predict the lithium plating onset conditions, as they provide information about the local potentials and lithium concentrations within the individual electrodes. Due to the narrow potential window of graphite, a precise determination of the sensitive parameters is needed for an accurate prediction of the plating onset. Experimental parameterization is needed as each cell has a specific geometry and the transport parameters are material and geometry-dependent. Literature values are scattered and often do not provide information on the electrode geometry. In this study, a non-isothermal electrochemical 3D model was experimentally parameterized and used to investigate the lithium plating onset at low temperatures. The whole set of geometrical, transport and kinetic model parameters were determined at different temperatures and states of charge and the results were validated against the individual potentials of a multi-layer pouch cell. Good predictions of lithium plating onset were obtained. The study shows that the model can be used to develop fast-charging strategies for commercial lithium-ion batteries at low temperatures.

Suggested Citation

  • Angeles Cabañero, Maria & Altmann, Johannes & Gold, Lukas & Boaretto, Nicola & Müller, Jana & Hein, Simon & Zausch, Jochen & Kallo, Josef & Latz, Arnulf, 2019. "Investigation of the temperature dependence of lithium plating onset conditions in commercial Li-ion batteries," Energy, Elsevier, vol. 171(C), pages 1217-1228.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:1217-1228
    DOI: 10.1016/j.energy.2019.01.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219300192
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.01.017?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. Ecker, Madeleine & Shafiei Sabet, Pouyan & Sauer, Dirk Uwe, 2017. "Influence of operational condition on lithium plating for commercial lithium-ion batteries – Electrochemical experiments and post-mortem-analysis," Applied Energy, Elsevier, vol. 206(C), pages 934-946.
    2. Yang, Fangfang & Wang, Dong & Zhao, Yang & Tsui, Kwok-Leung & Bae, Suk Joo, 2018. "A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries," Energy, Elsevier, vol. 145(C), pages 486-495.
    3. Berrueta, Alberto & Urtasun, Andoni & Ursúa, Alfredo & Sanchis, Pablo, 2018. "A comprehensive model for lithium-ion batteries: From the physical principles to an electrical model," Energy, Elsevier, vol. 144(C), pages 286-300.
    4. Cui, Yingzhi & Zuo, Pengjian & Du, Chunyu & Gao, Yunzhi & Yang, Jie & Cheng, Xinqun & Ma, Yulin & Yin, Geping, 2018. "State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method," Energy, Elsevier, vol. 144(C), pages 647-656.
    5. Deng, Zhongwei & Yang, Lin & Deng, Hao & Cai, Yishan & Li, Dongdong, 2018. "Polynomial approximation pseudo-two-dimensional battery model for online application in embedded battery management system," Energy, Elsevier, vol. 142(C), pages 838-850.
    6. Simone Barcellona & Luigi Piegari, 2017. "Lithium Ion Battery Models and Parameter Identification Techniques," Energies, MDPI, vol. 10(12), pages 1-24, December.
    7. Chu, Zhengyu & Feng, Xuning & Lu, Languang & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2017. "Non-destructive fast charging algorithm of lithium-ion batteries based on the control-oriented electrochemical model," Applied Energy, Elsevier, vol. 204(C), pages 1240-1250.
    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. Li, Junqiu & Sun, Danni & Jin, Xin & Shi, Wentong & Sun, Chao, 2019. "Lithium-ion battery overcharging thermal characteristics analysis and an impedance-based electro-thermal coupled model simulation," Applied Energy, Elsevier, vol. 254(C).
    2. He, Rong & He, Yongling & Xie, Wenlong & Guo, Bin & Yang, Shichun, 2023. "Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy," Energy, Elsevier, vol. 263(PD).
    3. Ouyang, Tiancheng & Xu, Peihang & Chen, Jingxian & Su, Zixiang & Huang, Guicong & Chen, Nan, 2021. "A novel state of charge estimation method for lithium-ion batteries based on bias compensation," Energy, Elsevier, vol. 226(C).
    4. Nithin Somasundaran & Nessa Fereshteh Saniee & Truong Quang Dinh & James Marco, 2023. "Study on the Extensibility of Voltage-Plateau-Based Lithium Plating Detection for Electric Vehicles," Energies, MDPI, vol. 16(6), pages 1-15, March.
    5. Sanaz Momeni Boroujeni & Kai Peter Birke, 2019. "Study of a Li-Ion Cell Kinetics in Five Regions to Predict Li Plating Using a Pseudo Two-Dimensional Model," Sustainability, MDPI, vol. 11(22), pages 1-14, November.
    6. Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
    7. Li, Yalun & Gao, Xinlei & Feng, Xuning & Ren, Dongsheng & Li, Yan & Hou, Junxian & Wu, Yu & Du, Jiuyu & Lu, Languang & Ouyang, Minggao, 2022. "Battery eruption triggered by plated lithium on an anode during thermal runaway after fast charging," Energy, Elsevier, vol. 239(PB).
    8. Liang, Jialin & Gan, Yunhua & Li, Yong & Tan, Meixian & Wang, Jianqin, 2019. "Thermal and electrochemical performance of a serially connected battery module using a heat pipe-based thermal management system under different coolant temperatures," Energy, Elsevier, vol. 189(C).
    9. Wu, Hongfei & Zhang, Xingjuan & Cao, Renfeng & Yang, Chunxin, 2021. "An investigation on electrical and thermal characteristics of cylindrical lithium-ion batteries at low temperatures," Energy, Elsevier, vol. 225(C).
    10. Liang, Jialin & Gan, Yunhua & Tan, Meixian & Li, Yong, 2020. "Multilayer electrochemical-thermal coupled modeling of unbalanced discharging in a serially connected lithium-ion battery module," Energy, Elsevier, vol. 209(C).

    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. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.
    2. Gao, Yizhao & Zhu, Chong & Zhang, Xi & Guo, Bangjun, 2021. "Implementation and evaluation of a practical electrochemical- thermal model of lithium-ion batteries for EV battery management system," Energy, Elsevier, vol. 221(C).
    3. 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.
    4. Liming Deng & Wenjing Shen & Kangkang Xu & Xuhui Zhang, 2024. "An Adaptive Modeling Method for the Prognostics of Lithium-Ion Batteries on Capacity Degradation and Regeneration," Energies, MDPI, vol. 17(7), pages 1-15, April.
    5. Pietro Iurilli & Luigi Luppi & Claudio Brivio, 2022. "Non-Invasive Detection of Lithium-Metal Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-14, September.
    6. Ghorbanzadeh, Milad & Astaneh, Majid & Golzar, Farzin, 2019. "Long-term degradation based analysis for lithium-ion batteries in off-grid wind-battery renewable energy systems," Energy, Elsevier, vol. 166(C), pages 1194-1206.
    7. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    8. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    9. Zhuangzhuang Cui & Zhuangzhuang Jia & Digen Ruan & Qingshun Nian & Jiajia Fan & Shunqiang Chen & Zixu He & Dazhuang Wang & Jinyu Jiang & Jun Ma & Xing Ou & Shuhong Jiao & Qingsong Wang & Xiaodi Ren, 2024. "Molecular anchoring of free solvents for high-voltage and high-safety lithium metal batteries," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    10. Jing Hou & He He & Yan Yang & Tian Gao & Yifan Zhang, 2019. "A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(9), pages 1-23, May.
    11. David Marcos & Maitane Garmendia & Jon Crego & José Antonio Cortajarena, 2021. "Functional Safety BMS Design Methodology for Automotive Lithium-Based Batteries," Energies, MDPI, vol. 14(21), pages 1-19, October.
    12. He, Rong & He, Yongling & Xie, Wenlong & Guo, Bin & Yang, Shichun, 2023. "Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy," Energy, Elsevier, vol. 263(PD).
    13. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
    14. Singh, Karanjot & Tjahjowidodo, Tegoeh & Boulon, Loïc & Feroskhan, Mir, 2022. "Framework for measurement of battery state-of-health (resistance) integrating overpotential effects and entropy changes using energy equilibrium," Energy, Elsevier, vol. 239(PA).
    15. Qi Wang & Tian Gao & Xingcan Li, 2022. "SOC Estimation of Lithium-Ion Battery Based on Equivalent Circuit Model with Variable Parameters," Energies, MDPI, vol. 15(16), pages 1-15, August.
    16. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    17. Huang, Zhelin & Xu, Fan & Yang, Fangfang, 2023. "State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model," Energy, Elsevier, vol. 262(PB).
    18. Shrivastava, Prashant & Soon, Tey Kok & Idris, Mohd Yamani Idna Bin & Mekhilef, Saad, 2019. "Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    19. Liang Zhang & Shunli Wang & Daniel-Ioan Stroe & Chuanyun Zou & Carlos Fernandez & Chunmei Yu, 2020. "An Accurate Time Constant Parameter Determination Method for the Varying Condition Equivalent Circuit Model of Lithium Batteries," Energies, MDPI, vol. 13(8), pages 1-12, April.
    20. Jinhyeong Park & Munsu Lee & Gunwoo Kim & Seongyun Park & Jonghoon Kim, 2020. "Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH," Energies, MDPI, vol. 13(9), pages 1-20, April.

    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:171:y:2019:i:c:p:1217-1228. 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.