IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i17p5434-d626886.html
   My bibliography  Save this article

Development of an Informative Lithium-Ion Battery Datasheet

Author

Listed:
  • Weiping Diao

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Chetan Kulkarni

    (KBR. Inc., NASA Ames Research Center, Moffett Field, CA 94035, USA)

  • Michael Pecht

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

Abstract

Lithium-ion battery datasheets, also known as specification sheets, are documents that battery manufacturers provide to define the battery’s function, operational limit, performance, reliability, safety, cautions, prohibitions, and warranty. Product manufacturers and customers rely on the datasheets for battery selection and battery management. However, battery datasheets often have ambiguous and, in many cases, misleading terminology and data. This paper reviews and evaluates the datasheets of 25 different lithium-ion battery types from eleven major battery manufacturers. Issues that customers may face are discussed, and recommendations for developing an informative and valuable datasheet that will help customers procure suitable batteries are presented.

Suggested Citation

  • Weiping Diao & Chetan Kulkarni & Michael Pecht, 2021. "Development of an Informative Lithium-Ion Battery Datasheet," Energies, MDPI, vol. 14(17), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5434-:d:626886
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/17/5434/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/17/5434/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Zeyu & Xiong, Rui & Lu, Jiahuan & Li, Xinggang, 2018. "Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 213(C), pages 375-383.
    2. Haibo Huo & Yinjiao Xing & Michael Pecht & Benno J. Züger & Neeta Khare & Andrea Vezzini, 2017. "Safety Requirements for Transportation of Lithium Batteries," Energies, MDPI, vol. 10(6), pages 1-38, June.
    3. Weiping Diao & Saurabh Saxena & Bongtae Han & Michael Pecht, 2019. "Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells," Energies, MDPI, vol. 12(15), pages 1-9, July.
    4. Saurabh Saxena & Darius Roman & Valentin Robu & David Flynn & Michael Pecht, 2021. "Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning," Energies, MDPI, vol. 14(3), pages 1-17, January.
    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. Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.

    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. 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).
    2. Li, Xiaoyu & Zhang, Zuguang & Wang, Wenhui & Tian, Yong & Li, Dong & Tian, Jindong, 2020. "Multiphysical field measurement and fusion for battery electric-thermal-contour performance analysis," Applied Energy, Elsevier, vol. 262(C).
    3. Xinwei Cong & Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yan Jiang & Linjing Zhang, 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-21, February.
    4. Chen, Zeyu & Zhang, Bo & Xiong, Rui & Shen, Weixiang & Yu, Quanqing, 2021. "Electro-thermal coupling model of lithium-ion batteries under external short circuit," Applied Energy, Elsevier, vol. 293(C).
    5. Jiahui Zhao & Yong Zhu & Bin Zhang & Mingyi Liu & Jianxing Wang & Chenghao Liu & Xiaowei Hao, 2023. "Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    6. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    7. Lichuan Wei & Yanhui Zou & Feng Cao & Zhendi Ma & Zhao Lu & Liwen Jin, 2022. "An Optimization Study on the Operating Parameters of Liquid Cold Plate for Battery Thermal Management of Electric Vehicles," Energies, MDPI, vol. 15(23), pages 1-24, December.
    8. 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.
    9. An, Zhoujian & Zhao, Yabing & Du, Xiaoze & Shi, Tianlu & Zhang, Dong, 2023. "Experimental research on thermal-electrical behavior and mechanism during external short circuit for LiFePO4 Li-ion battery," Applied Energy, Elsevier, vol. 332(C).
    10. Xie, Jiahang & Yang, Rufan & Gooi, Hoay Beng & Nguyen, Hung Dinh, 2023. "PID-based CNN-LSTM for accuracy-boosted virtual sensor in battery thermal management system," Applied Energy, Elsevier, vol. 331(C).
    11. Li, Alan G. & West, Alan C. & Preindl, Matthias, 2022. "Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review," Applied Energy, Elsevier, vol. 316(C).
    12. 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).
    13. Ren, Dongsheng & Liu, Xiang & Feng, Xuning & Lu, Languang & Ouyang, Minggao & Li, Jianqiu & He, Xiangming, 2018. "Model-based thermal runaway prediction of lithium-ion batteries from kinetics analysis of cell components," Applied Energy, Elsevier, vol. 228(C), pages 633-644.
    14. Zhu, Rui & Duan, Bin & Zhang, Chenghui & Gong, Sizhao, 2019. "Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    15. Li, Marui & Dong, Chaoyu & Xiong, Binyu & Mu, Yunfei & Yu, Xiaodan & Xiao, Qian & Jia, Hongjie, 2022. "STTEWS: A sequential-transformer thermal early warning system for lithium-ion battery safety," Applied Energy, Elsevier, vol. 328(C).
    16. Zhang, Wencan & Ouyang, Nan & Yin, Xiuxing & Li, Xingyao & Wu, Weixiong & Huang, Liansheng, 2022. "Data-driven early warning strategy for thermal runaway propagation in Lithium-ion battery modules with variable state of charge," Applied Energy, Elsevier, vol. 323(C).
    17. Yang, Jiong & Cheng, Fanyong & Liu, Zhi & Duodu, Maxwell Mensah & Zhang, Mingyan, 2023. "A novel semi-supervised fault detection and isolation method for battery system of electric vehicles," Applied Energy, Elsevier, vol. 349(C).
    18. Nicholas Gordon Garafolo & Siamak Farhad & Manindra Varma Koricherla & Shihao Wen & Roja Esmaeeli, 2022. "Modal Analysis of a Lithium-Ion Battery for Electric Vehicles," Energies, MDPI, vol. 15(13), pages 1-11, July.
    19. Haber, Marc & Azaïs, Philippe & Genies, Sylvie & Raccurt, Olivier, 2023. "Stress factor identification and Risk Probabilistic Number (RPN) analysis of Li-ion batteries based on worldwide electric vehicle usage," Applied Energy, Elsevier, vol. 343(C).
    20. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).

    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:14:y:2021:i:17:p:5434-:d:626886. 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.