IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v305y2022ics0306261921012320.html
   My bibliography  Save this article

Parameter sensitivity analysis of a reduced-order electrochemical-thermal model for heat generation rate of lithium-ion batteries

Author

Listed:
  • Song, Minseok
  • Choe, Song-Yul

Abstract

A reduced-order electrochemical-thermal model is capable of accurately estimating heat generation rate of lithium-ion batteries with high accuracy. However, as the number of parameters drastically increases, an identification of the parameters becomes essential but challenging. In this paper, a sensitivity analysis of its physical parameters is conducted to study the influence of the parameters on the heat generation rate for the first time. First, 23 potential parameters are selected, where the ranges of the parameters are assumed based on the literature. The parameters are then varied, and the corresponding heat generation rate is calculated to quantify their effects. The analysis has shown various tendencies of the sensitivity of the parameters, and their behavior is explained from further analysis. Based on their sensitivities, the parameters are clustered into three groups dependent upon dominant ranges of state of charge, where insensitive parameters are excluded. Finally, the parameters in each group are identified in their dominant ranges of the state of charge separately based on a three-stage identification procedure. For the identification, the genetic algorithm is used, where the objective function is to minimize the model error for the battery temperature. The results of the identification have shown that the parameters are converged within an optimal solution. The model with the identified parameters is further validated against the experimental data using a pouch type large format lithium-manganese-cobalt-oxide/ Graphite cell at constant charge and discharge profiles over the full range of the state of charge and driving cycles, which has shown a good agreement.

Suggested Citation

  • Song, Minseok & Choe, Song-Yul, 2022. "Parameter sensitivity analysis of a reduced-order electrochemical-thermal model for heat generation rate of lithium-ion batteries," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012320
    DOI: 10.1016/j.apenergy.2021.117920
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921012320
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117920?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. Bi, Yalan & Choe, Song-Yul, 2020. "An adaptive sigma-point Kalman filter with state equality constraints for online state-of-charge estimation of a Li(NiMnCo)O2/Carbon battery using a reduced-order electrochemical model," Applied Energy, Elsevier, vol. 258(C).
    2. Yin, Yilin & Choe, Song-Yul, 2020. "Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control," Applied Energy, Elsevier, vol. 271(C).
    3. Wenwu Tang & Meijuan Jia, 2014. "Global Sensitivity Analysis of a Large Agent-Based Model of Spatial Opinion Exchange: A Heterogeneous Multi-GPU Acceleration Approach," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 104(3), pages 485-509, May.
    4. Li, Weihan & Cao, Decheng & Jöst, Dominik & Ringbeck, Florian & Kuipers, Matthias & Frie, Fabian & Sauer, Dirk Uwe, 2020. "Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries," Applied Energy, Elsevier, vol. 269(C).
    5. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
    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. Verma, Ashima & Saikia, Tanmoy & Saikia, Pranaynil & Rakshit, Dibakar & Ugalde-Loo, Carlos E., 2023. "Thermal performance analysis and experimental verification of lithium-ion batteries for electric vehicle applications through optimized inclined mini-channels," Applied Energy, Elsevier, vol. 335(C).
    2. Lin, Xiang-Wei & Li, Yu-Bai & Wu, Wei-Tao & Zhou, Zhi-Fu & Chen, Bin, 2024. "Advances on two-phase heat transfer for lithium-ion battery thermal management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    3. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
    4. Li, Yuanmao & Liu, Guixiong & Deng, Wei & Li, Zuyu, 2024. "Comparative study on parameter identification of an electrochemical model for lithium-ion batteries via meta-heuristic methods," Applied Energy, Elsevier, vol. 367(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. Lai, Qingzhi & Ahn, Hyoung Jun & Kim, YoungJin & Kim, You Na & Lin, Xinfan, 2021. "New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery," Applied Energy, Elsevier, vol. 295(C).
    2. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
    3. Gao, Yizhao & Liu, Chenghao & Chen, Shun & Zhang, Xi & Fan, Guodong & Zhu, Chong, 2022. "Development and parameterization of a control-oriented electrochemical model of lithium-ion batteries for battery-management-systems applications," Applied Energy, Elsevier, vol. 309(C).
    4. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    5. Gu, Yuxuan & Wang, Jianxiao & Chen, Yuanbo & Xiao, Wei & Deng, Zhongwei & Chen, Qixin, 2023. "A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control," Energy, Elsevier, vol. 264(C).
    6. 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.
    7. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    8. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
    9. 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.
    10. Motoaki, Yutaka & Yi, Wenqi & Salisbury, Shawn, 2018. "Empirical analysis of electric vehicle fast charging under cold temperatures," Energy Policy, Elsevier, vol. 122(C), pages 162-168.
    11. Ragab El-Sehiemy & Mohamed A. Hamida & Ehab Elattar & Abdullah Shaheen & Ahmed Ginidi, 2022. "Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm," Energies, MDPI, vol. 15(13), pages 1-20, June.
    12. 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).
    13. Fan, Zhaohui & Fu, Yijie & Liang, Hong & Gao, Renjing & Liu, Shutian, 2023. "A module-level charging optimization method of lithium-ion battery considering temperature gradient effect of liquid cooling and charging time," Energy, Elsevier, vol. 265(C).
    14. Xu, Yuan-wu & Wu, Xiao-long & Zhong, Xiao-bo & Zhao, Dong-qi & Sorrentino, Marco & Jiang, Jianhua & Jiang, Chang & Fu, Xiaowei & Li, Xi, 2021. "Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage," Applied Energy, Elsevier, vol. 286(C).
    15. Qin, Yudi & Du, Jiuyu & Lu, Languang & Gao, Ming & Haase, Frank & Li, Jianqiu & Ouyang, Minggao, 2020. "A rapid lithium-ion battery heating method based on bidirectional pulsed current: Heating effect and impact on battery life," Applied Energy, Elsevier, vol. 280(C).
    16. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    17. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    18. Li, Yong & Wang, Liye & Feng, Yanbiao & Liao, Chenglin & Yang, Jue, 2024. "An online state-of-health estimation method for lithium-ion battery based on linear parameter-varying modeling framework," Energy, Elsevier, vol. 298(C).
    19. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
    20. Tao, Laifa & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou & Noktehdan, Azadeh, 2017. "Lithium-ion battery capacity fading dynamics modelling for formulation optimization: A stochastic approach to accelerate the design process," Applied Energy, Elsevier, vol. 202(C), pages 138-152.

    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:305:y:2022:i:c:s0306261921012320. 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.