IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v175y2016icp522-528.html
   My bibliography  Save this article

Facile large-scale synthesis of core–shell structured sulfur@polypyrrole composite and its application in lithium–sulfur batteries with high energy density

Author

Listed:
  • Xie, Yanping
  • Zhao, Hongbin
  • Cheng, Hongwei
  • Hu, Chenji
  • Fang, Wenying
  • Fang, Jianhui
  • Xu, Jiaqiang
  • Chen, Zhongwei

Abstract

In this context, we report a novel one-pot synthesis of S@Polypyrrole core–shell spheres as the cathode material in lithium–sulfur battery, designed by polypyrrole (PPy) wrapping on the formed nano-sulfur via the facile wet chemical strategy. The in-situ polymerized PPy layer in this special structure is helpful to inhibit aggregation of sulfur particles with small size, the polysulfide dissolution and shuttling, and offer fast and efficient transport of electron/lithium ion within the electrode. Besides, the flexible PPy layer effectively accommodates the volume expansion. PPy with partial PO43− doping was also employed to improve cycling stability and C-rate performance of S@PPy composite by exchanging Cl− with PO43−. Thus, the sulfur electrode with a high sulfur loading of 80% delivered an initial discharge capacity of 1142mAhg−1 and maintained a high capacity of 805mAhg−1 after 50 cycles at 0.1C. The corresponding capacity retention over 100 cycles was about 65%, with a limited decay of 0.3% per cycle. Even at a high current density of 1.5C, the composite still exhibited a high discharge capacity of about 700mAhg−1. This results mean that the designed electrode can achieve a high practical specific energy density of more than 400Wh/kg, far beyond the commercial LiCoO2 batteries. Due to low cost, facile large-scale synthesis and superior electrochemical performance of S@PPy cathode with high sulfur loading, this work will provide a very promising method to further promote the Li–S batteries in the practical application of portable electronic devices, electric devices and energy storage system.

Suggested Citation

  • Xie, Yanping & Zhao, Hongbin & Cheng, Hongwei & Hu, Chenji & Fang, Wenying & Fang, Jianhui & Xu, Jiaqiang & Chen, Zhongwei, 2016. "Facile large-scale synthesis of core–shell structured sulfur@polypyrrole composite and its application in lithium–sulfur batteries with high energy density," Applied Energy, Elsevier, vol. 175(C), pages 522-528.
  • Handle: RePEc:eee:appene:v:175:y:2016:i:c:p:522-528
    DOI: 10.1016/j.apenergy.2016.03.085
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261916304111
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2016.03.085?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. Xiong, Rui & Sun, Fengchun & Gong, Xianzhi & Gao, Chenchen, 2014. "A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 1421-1433.
    2. Liu, Guangming & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Hua, Jianfeng, 2015. "A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications," Applied Energy, Elsevier, vol. 149(C), pages 297-314.
    3. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2014. "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, Elsevier, vol. 135(C), pages 81-87.
    4. Zhiyu Wang & Yanfeng Dong & Hongjiang Li & Zongbin Zhao & Hao Bin Wu & Ce Hao & Shaohong Liu & Jieshan Qiu & Xiong Wen (David) Lou, 2014. "Enhancing lithium–sulphur battery performance by strongly binding the discharge products on amino-functionalized reduced graphene oxide," Nature Communications, Nature, vol. 5(1), pages 1-8, December.
    5. Liu, Xingtao & Chen, Zonghai & Zhang, Chenbin & Wu, Ji, 2014. "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Elsevier, vol. 123(C), pages 263-272.
    6. Wang, Xue & Deng, Jinxing & Duan, Xiaojuan & Liu, Dong & Liu, Peng, 2015. "Fluorescent brightener CBS-X doped polypyrrole as smart electrode material for supercapacitors," Applied Energy, Elsevier, vol. 153(C), pages 70-77.
    7. Su, Y. & Zhitomirsky, I., 2015. "Asymmetric electrochemical supercapacitor, based on polypyrrole coated carbon nanotube electrodes," Applied Energy, Elsevier, vol. 153(C), pages 48-55.
    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. Capkova, Dominika & Knap, Vaclav & Fedorkova, Andrea Strakova & Stroe, Daniel-Ioan, 2023. "Investigation of the temperature and DOD effect on the performance-degradation behavior of lithium–sulfur pouch cells during calendar aging," Applied Energy, Elsevier, vol. 332(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. Avvari, G.V. & Pattipati, B. & Balasingam, B. & Pattipati, K.R. & Bar-Shalom, Y., 2015. "Experimental set-up and procedures to test and validate battery fuel gauge algorithms," Applied Energy, Elsevier, vol. 160(C), pages 404-418.
    2. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2017. "On-line battery state-of-charge estimation based on an integrated estimator," Applied Energy, Elsevier, vol. 185(P2), pages 2026-2032.
    3. Xiangyu Cui & Zhu Jing & Maji Luo & Yazhou Guo & Huimin Qiao, 2018. "A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter," Energies, MDPI, vol. 11(1), pages 1-21, January.
    4. Shifei Yuan & Hongjie Wu & Xuerui Ma & Chengliang Yin, 2015. "Stability Analysis for Li-Ion Battery Model Parameters and State of Charge Estimation by Measurement Uncertainty Consideration," Energies, MDPI, vol. 8(8), pages 1-23, July.
    5. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    6. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    7. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    8. Tang, Xiaopeng & Liu, Boyang & Lv, Zhou & Gao, Furong, 2017. "Observer based battery SOC estimation: Using multi-gain-switching approach," Applied Energy, Elsevier, vol. 204(C), pages 1275-1283.
    9. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    10. Wei, Zhongbao & Lim, Tuti Mariana & Skyllas-Kazacos, Maria & Wai, Nyunt & Tseng, King Jet, 2016. "Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery," Applied Energy, Elsevier, vol. 172(C), pages 169-179.
    11. Ansari, Amir Babak & Esfahanian, Vahid & Torabi, Farschad, 2016. "Discharge, rest and charge simulation of lead-acid batteries using an efficient reduced order model based on proper orthogonal decomposition," Applied Energy, Elsevier, vol. 173(C), pages 152-167.
    12. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai & Xie, Jing & Zhang, Xu, 2015. "A novel active equalization method for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 145(C), pages 36-42.
    13. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2015. "A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy," Applied Energy, Elsevier, vol. 137(C), pages 427-434.
    14. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    15. Oh, Ki-Yong & Epureanu, Bogdan I., 2016. "Characterization and modeling of the thermal mechanics of lithium-ion battery cells," Applied Energy, Elsevier, vol. 178(C), pages 633-646.
    16. Lin, Cheng & Mu, Hao & Xiong, Rui & Cao, Jiayi, 2017. "Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy," Applied Energy, Elsevier, vol. 194(C), pages 560-568.
    17. Liu, Guangming & Ouyang, Minggao & Lu, Languang & Li, Jianqiu & Hua, Jianfeng, 2015. "A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications," Applied Energy, Elsevier, vol. 149(C), pages 297-314.
    18. Farmann, Alexander & Sauer, Dirk Uwe, 2018. "Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 225(C), pages 1102-1122.
    19. Xiaopeng Tang & Ke Yao & Boyang Liu & Wengui Hu & Furong Gao, 2018. "Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-16, January.
    20. Wei, Zhongbao & Meng, Shujuan & Xiong, Binyu & Ji, Dongxu & Tseng, King Jet, 2016. "Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer," Applied Energy, Elsevier, vol. 181(C), pages 332-341.

    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:appene:v:175:y:2016:i:c:p:522-528. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.