IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5687-d1205446.html
   My bibliography  Save this article

Impact of the Scan Rate on the Stability Window of an Electrical Double-Layer Capacitor

Author

Listed:
  • Charles Cougnon

    (Univ Angers, CNRS, MOLTECH-ANJOU, SFR MATRIX, F-49000 Angers, France)

Abstract

Because of the intermittent nature of renewable energy sources, such as solar or wind power, energy storage is becoming strategic for securing the energy transition. In this context, energy storage technologies must become robust, secure, and efficient, so that they become attractive and competitive solutions. For these reasons, the stability of storage systems is a matter which must concern us. In the supercapacitor community, methodologies used to evaluate the stability window are widely discussed and debated, but the way it is impacted by the charging regime is rarely investigated. This question is even more important as the stability window is only valid for the current rate at which it was determined. In this study, the impact of the charging regime on the stability window was investigated both qualitatively and quantitatively by cyclic voltammetry. Results clearly demonstrate that the stability window reduces as the scan rate decreases. This is because degradation processes can be viewed as the result of irreversible electrochemical processes. In severe cases, this reduction in stability is such that it can be fatal for the supercapacitor if such a change in the charging regime has not been anticipated.

Suggested Citation

  • Charles Cougnon, 2023. "Impact of the Scan Rate on the Stability Window of an Electrical Double-Layer Capacitor," Energies, MDPI, vol. 16(15), pages 1-11, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5687-:d:1205446
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5687/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5687/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Jie Xiao & Qiuyan Li & Yujing Bi & Mei Cai & Bruce Dunn & Tobias Glossmann & Jun Liu & Tetsuya Osaka & Ryuta Sugiura & Bingbin Wu & Jihui Yang & Ji-Guang Zhang & M. Stanley Whittingham, 2020. "Understanding and applying coulombic efficiency in lithium metal batteries," Nature Energy, Nature, vol. 5(8), pages 561-568, August.
    Full references (including those not matched with items on IDEAS)

    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. Pietro Iurilli & Luigi Luppi & Claudio Brivio, 2022. "Non-Invasive Detection of Lithium-Metal Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-14, September.
    2. Lai, Xin & Zhou, Long & Zhu, Zhiwei & Zheng, Yuejiu & Sun, Tao & Shen, Kai, 2023. "Experimental investigation on the characteristics of coulombic efficiency of lithium-ion batteries considering different influencing factors," Energy, Elsevier, vol. 274(C).
    3. 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.
    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. 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.
    6. 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.
    7. Chang, Chun & Li, Xinqi & Sun, Yuhui & Jiang, Jiuchun & Tian, Aina & Lv, Lu & Gao, Yang, 2025. "Force-signal driven real-time lithium plating detection in mechanically constrained LiFePO4 pouch cells," Energy, Elsevier, vol. 323(C).
    8. 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.
    9. 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).
    10. 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.
    11. Matthew Burton & Sudarshan Narayanan & Ben Jagger & Lorenz F. Olbrich & Shobhan Dhir & Masafumi Shibata & Michael J. Lain & Robert Astbury & Nicholas Butcher & Mark Copley & Toshikazu Kotaka & Yuichi , 2025. "Techno-economic assessment of thin lithium metal anodes for solid-state batteries," Nature Energy, Nature, vol. 10(1), pages 135-147, January.
    12. Akash Samanta & Sheldon S. Williamson, 2021. "A Comprehensive Review of Lithium-Ion Cell Temperature Estimation Techniques Applicable to Health-Conscious Fast Charging and Smart Battery Management Systems," Energies, MDPI, vol. 14(18), pages 1-25, September.
    13. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
    14. 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).
    15. Yang, Fangfang & Song, Xiangbao & Dong, Guangzhong & Tsui, Kwok-Leung, 2019. "A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries," Energy, Elsevier, vol. 171(C), pages 1173-1182.
    16. 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.
    17. repec:cdl:itsdav:qt5zx1k22k is not listed on IDEAS
    18. Salimeh Gohari & Vaclav Knap & Mohammad Reza Yaftian, 2021. "Investigation on Cycling and Calendar Aging Processes of 3.4 Ah Lithium-Sulfur Pouch Cells," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    19. Xuebing Han & Shuoyuan Mao & Yu Wang & Yao Lu & Depeng Wang & Yukun Sun & Yuejiu Zheng & Xuning Feng & Languang Lu & Jianfeng Hua & Minggao Ouyang, 2025. "Manipulation of lithium dendrites based on electric field relaxation enabling safe and long-life lithium-ion batteries," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    20. 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).
    21. Seongjae Ko & Xiao Han & Tatau Shimada & Norio Takenaka & Yuki Yamada & Atsuo Yamada, 2023. "Electrolyte design for lithium-ion batteries with a cobalt-free cathode and silicon oxide anode," Nature Sustainability, Nature, vol. 6(12), pages 1705-1714, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jeners:v:16:y:2023:i:15:p:5687-:d:1205446. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.