شبیه‌سازی الکتروشیمیایی-حرارتی سلول باتری‌ لیتیوم-یون خودروی الکتریکی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده مهندسی مکانیک دانشگاه تربیت دبیر شهید رجایی

چکیده

امروزه مشکلاتی مانند تولید آلاینده­ها و کاهش منابع فسیلی از جمله مشکلات اصلی جهانی شده است. تعداد زیاد خودرو­‌هایی که از سوخت­های هیدروکربنی استفاده می­کنند اصلی‌ترین عامل بروز این مشکلات است. استفاده از خودرو­های الکتریکی یک راه‌حل مناسب محسوب می­شود.یکی از مهم­ترین مؤلفه‌ها برای باتری خودروهای الکتریکی محاسبۀ میزان سطح شارژ آن است. در این مقاله یک تک‌سلول باتری لیتیومی منشوری حین چرخۀ تخلیه به روش الکتروشیمیایی-حرارتی با استفاده از دینامیک سیالات محاسباتی سه‌بعدی شبیه‌سازی‌شده است. ابتدا نتایج شبیه­ سازی با نتایج آزمایشگاهی ولتاژ و دما اعتبار­سنجی شده است. سپس نتایج توزیع پتانسیل الکتریکی در جمع‌کننده جریان، سطح شارژ، غلظت یون لیتیوم در الکترود، توزیع دما و حرارت­های تولیدشده در باتری آورده شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Electrochemical-Thermal Simulation of Cell Lithium-Ion Battery of Electrical Vehicle

نویسندگان [English]

  • Mohammad Amir Bayati Nezhad
  • Arash Mohammadi
Mechanical Engineering Department, Shahid Rajaee Teacher Training University.
چکیده [English]

Nowadays, pollution and reduction in fossil resources are major problems for world.The large number of cars which use hydrocarbons fuels is the main factor of this problem. Using of electric vehicles is a suitable solution. One of the most important parameters to calculate for electric vehicles battery is its state of charge. In this paper, a single prismatic cell of lithium ion battery in discharge cycle with electrochemical-thermal has been simulated with 3D CFD. First, numerical results are validated with experimental results of voltage and temperature. Then, the results of the electrical potential distribution in the current collector, state of charge, the lithium ion concentration in electrode, the temperature distribution and generated heat in battery are presented.

کلیدواژه‌ها [English]

  • Lithium Ion Battery
  • State of Charge
  • Lithium Ion Concentration
1.    Rahn, C. D., and Wang, C. Y., "Battery Systems Engineering", John Wiley & Sons Ltd, (2013).
2.    Mastali Majdabadi, M., "Electrochemical-Thermal Modeling of Lithium-ion Batteries", PhD thesis, University of Waterloo, (2016).
3.    Linden, D., and Reddy, T. B., "Handbook of Batteries", 2nd ed, McGraw-Hill, New York, (2002).
4.    Martin, H., "Solid-state-EV-Battery Breakthrough from Li-ion Battery Inventor John Goodenough", North American Energy News, (2017).
5.    Campestrini, C., and Horsche, M. F., "Validation and Benchmark Methods for Battery Management System Functionalities: State of Charge Estimation Algorithms", Journal Energy Storage, Vol. 7, No. 1,  pp. 38-51, (2016).
6.    Hannan, M. A., and Lipu, M. S. H., "A Review of Lithium-ion Battery State of Charge Estimation and Management System in Electric Vehicle Applications: Challenges and Recommendations", Renewable and Sustainable Energy Reviews, Vol. 78, No. 1, pp. 834–854, (2017).
7.    Chung, S. Y., Bloking, J. T., and Chiang, Y. M., "Electronically Conductive Phospho-olivines as Lithium Storage Electrodes", Nature Materials, Vol. 1, No. 2, pp. 123–128, (2002).
8.    Conte, F. V., "Battery and Battery Management for Hybrid Electric Vehicles", A Review, Elektro Und Inf, Vol. 123, No. 10, pp. 424–31, (2006).
9.    Imara Corporation website, Imaracorp.com, Archived from the original on 22 July 2009, Retrieved 8 October, (2011).
10. Kroeze, R. C., and Krein, P. T., "Electrical Battery Model for Use in Dynamic Electric Vehicle Simulations", IEEE Power Electron Spec Conference, pp. 1336–1342, (2008).
11. Chen, M., and Rinc Mora, G. A., "Accurate Electrical Battery Model Capable of Predicting Runtime and I-V Performance", IEEE Trans Energy Convers, Vol. 21, No. 2, pp. 504–11, (2006).
12. Xing, Y., Ma, E. W. M., Tsui, K. L., and Pecht, M., "Battery Management Systems in Electric and Hybrid Vehicles", Energies, Vol. 4, No. 11, pp. 1840–57, (2011).
13. Xu, L., Wang, J., and Chen, Q., "Kalman Filtering State of Charge Estimation for Battery Management System Based on A Stochastic Fuzzy Neural Network Battery model", Energy Convers Manag, Vol. 53, No. 1, pp. 33–9, (2012).
14. Li, J., Yuan, C. F., Guo, Z. H., Zhang, Z. A., Lai, Y. Q., and Liu, J., "Limiting Factors for Low Temperature Performance of Electrolytes in LiFePO4/Li and Graphite/Li Half Cells", Electrochim Acta, Vol. 59, No. 1, pp. 69–74, (2012).
15. Ye, Y., Shi, Y., Cai, N., Lee, J., and He, X., "Electro-thermal modeling and experimental validation for lithium ion battery", Journal of Power Sources, Vol. 199, No. 1, pp. 227– 238, (2012).
16. Xing, Y., He, W., Pecht, M., and Tsui, K. L., "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures", Applied Energy, Vol. 113, No. 1, pp. 106–115, (2014).
17. Zhang, Y., "State-of-charge Estimation of the Llithium-ion Battery System with Time-Varying Parameter for Hybrid Electric Vehicles", Control TheoryAppl IET, Vol. 8, No. 3, pp. 160–7, (2014).
18. Cheng, M., Feng, L., and Chen, B., "Simulation of Lithium Ion HEV Battery Aging Using Electrochemical Battery Model under Different Ambient Temperature Conditions", SAE International by University of Alberta Libraries, (2015).
19. Bahiraei, F., Fartaj, A., and Nazri, Gh. A., "Electrochemical-thermal Modeling to Evaluate Active Thermal Management of a Lithium-ion Battery Module", Electrochimica Acta, Vol. 254, No. 1, pp. 59–71, (2017).
20. Tang, Y., and Wu, L., "Study of the Thermal Properties During the Cyclic Process of Lithium ion Power Batteries Using the Electrochemical-Thermal Coupling model", Applied Thermal Engineering, Vol. 137, No. 1, pp. 11–22, (2018).
21. Hosseinzadeh, E., Genieser, R., Worwood, D., Barai, A., Marco, J., and Jennings, P., "A systematic approach for electrochemical-thermal modelling of a large format lithium-ion battery for electric vehicle application", Journal of power sources, Vol. 382, No. 1, pp. 77-94, (2018).
22. Chen, C., Xiong, R., Yang, R., and Shen, W., "State of Charge Estimation of Lithium-ion Battery Using an Improved Neural Network Model and Extended Kalman Filter", Journal of Cleaner Production, Vol. 234, No. 1, pp. 1153-1164, (2019).
23. Fink, C., and Kaltenegger, B., "Electrothermal and Electrochemical Modeling of Lithium-ion Batteries: 3D Simulation with Experimental Validation", The Electrochemical Society, Vol. 61, No. 27, (2014).
24. Doyle, M., Newman, J., Gozdz, A., Schmutz, C., and Tarascon, J. M., "Comparison of modeling predictions with experimental data from plastic lithium ion cells", Journal of the Electrochemical Society, Vol. 143, No. 6, (1996(.
25. EIG-ePLB-C020-Datasheet, HighEnergy Product ePLB C Technology.
26. Taheri, P., and Bahrami, M., "Temperature Rise in Prismatic Polymer Lithium-Ion Batteries: An Analytic Approach",SAE Int, Vol. 5, No. 1, (2012).
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