تخمین سطح شارژِ مجموعه سلول لیتیومی با اتصال سری با استفاده از فیلتر کالمن تعمیم‌یافته

نوع مقاله : مقاله پژوهشی

نویسندگان

1 صنعتی اصفهان*مهندسی مکانیک

2 پژوهشکده خودرو، سوخت و محیط زیست، دانشگاه تهران، تهران، ایران

چکیده

جموعه باتری یکی از اجزای اصلی در خودروهای الکتریکی است که بهطور معمول از مجموعهای از سلولهای باتری تشکیل شده است که به صورت سری به یکدیگر متصل می شوند. یکی از مهمترین وظایف سیستم مدیریت باتری در خودروهای الکتریکی تخمین سطح شارژ مجموعه باتری است. سلول های موجود در یک پک باتری بدلیل تلرانس های مختلف ساخت و شرایط ً مختلف عملکردی الزاما سطح شارژ یکسانی ندارند و از این ً رو، سطح شارژ مجموعه باتری الزاما با سطح شارژ سلولها یکسان نیست. این مقاله به ارایه روشی برای تخمین سطح شارژ مجموعه باتری می ً پردازد که در کنار دقت باال، هزینه محاسباتی نسبتا پایینی دارد. ابتدا از روش شمارش کولمب و منحنی ولتاژ مدار باز باتری که از داده های تجربی استخراج شده است، به طور همزمان برای تخمین ِ سطح شارژ میانگین مجموعه باتری استفاده شده است. سپس با استفاده از فیلتر کالمن تعمیم یافته، اختالف سطح شارژ بین هر کدام از سلول ها و سطح شارژ میانگین تخمین زده شده است. روش پیشنهادی بوسیله یک بستر تست تجربی و برای مجموعه ای متشکل از سه سلول باتری لیتیومی که به شکل سری متصل شده اند مورد ارزیابی و صحه گذاری قرار گرفته است. نتایج تست های تجربی حاکی از عملکرد مناسب روش پیشنهادی در تخمین سطح شارژ پک باتری لیتیومی می باشد

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

State of Charge Estimation for Series-Connected Lithium Battery Pack Using Extended Kalman Filter

نویسندگان [English]

  • Mohsen Esfahanian 1
  • Mohammad Javad Esfandyari 2
  • Vahid Esfahanian 2
  • Hassan Nehzati 2
  • Haddad Miladi 2
1 Mechanical Engineering Dept.
2 Vehicle, Fuel and Environment Research Institute, University of Tehran, Tehran, Iran
چکیده [English]

The battery pack is one of the main components in electric vehicles which is usually composed of many cells connected in series. Battery state of charge estimation is one of the most important functions of the battery management system in electric vehicles. Due to the different manufacturing and operational conditions, all of the cells in a battery pack do not have the same states of charge and therefore, the cell and pack states of charge are not the same. This paper presents a method for battery pack state of charge estimation which benefits rather low computational cost as well as the high precision. First, the coulomb counting method and the open circuit voltage graph, which is obtained from the experimental results, are used simultaneously to estimate the pack average state of charge. Then, the extended Kalman filter method is used to estimate the difference between the pack average state of charge with those of the cells. The proposed method has been evaluated and verified using an experimental test bench for three series-connected lithium cells. Experimental test results indicate good performance of the proposed method in estimating the lithium battery pack state of charge.

کلیدواژه‌ها [English]

  • Battery State of Charge
  • Extended Kalman Filter
  • Lithium Battery Pack
  • Battery Management System
[1] V. Esfahanian, M. J. Esfandyari, M. R. Hairi Yazdi, H. Nehzati, “Design and Implementation of A Real-time Simulator for Hardware-in-the-Loop Testing of A Hybrid Electric Bus Central Control Unit,” FISITA World Automotive Congress, Maastrict, Netherland, June 2014.
[2] M. J. Esfandyari, V. Esfahanian, M. R. H. Yazdi, H. Nehzati, A. Salehi, “Design and Implementation of a Model-in-the-Loop Simulator for Verification of the Vehicle Control Software in a Series Hybrid Electric Bus,” Modares Mechanical Engineering, vol. 14, no. 12, pp. 13-22, 2015. (in Persian)
[3] M. J. Esfandyari, M. R. Ha'iri Yazdi, V. Esfahanian, H. Nehzati, “Design of a Real-time Simulator of the Engine-Generator for a Series Hybrid Electric Bus,” Modares Mechanical Engineering, vol. 14, no. 4, pp. 200-206, 2014. (in Persian)
[4] M. A. Hannan, M. S. H. Lipu, A. Hussain, and A. Mohamed, “A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations,” Renew. Sustain. Energy Rev., vol. 78, no. May, pp. 834–854, 2017.
[5] W. Waag, C. Fleischer, and D. U. Sauer, “Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles,” J. Power Sources, vol. 258, pp. 321–339, 2014.
[6] L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang, “A review on the key issues for lithium-ion battery management in electric vehicles,” J. Power Sources, vol. 226, pp. 272–288, Mar. 2013.
[7] M. Dubarry, V. Svoboda, R. Hwu, and B. Y. Liaw, “Capacity loss in rechargeable lithium cells during cycle life testing: The importance of determining state-of-charge,” J. Power Sources, vol. 174, no. 2, pp. 1121–1125, 2007.
[8] V. Pop, H. J. Bergveld, J. H. G. O. het Veld, P. P. L. Regtien, D. Danilov, and P. H. L. Notten, “Modeling battery behavior for accurate state-of-charge indication,” J. Electrochem. Soc., vol. 153, no. 11, pp. A2013–A2022, 2006.
[9] C. Speltino, D. Di Domenico, G. Fiengo, and A. Stefanopoulou, “Comparison of reduced order lithium-ion battery models for control applications,” Proc. 48h IEEE Conf. Decis. Control held jointly with 2009 28th Chinese Control Conf., pp. 3276–3281, 2009.
[10] X. Hu, S. Li, and H. Peng, “A comparative study of equivalent circuit models for Li-ion batteries,” J. Power Sources, vol. 198, pp. 359–367, 2012.
[11] F. Zhou, L. Wang, H. Lin, and Z. Lv, “High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on Adaptive Wavelet Neural Network,” in ECCE Asia Downunder (ECCE Asia), 2013 IEEE, 2013, pp. 513–517.
[12] W. Jian, X. Jiang, J. Zhang, Z. Xiang, and Y. Jian, “Comparison of SOC estimation performance with different training functions using neural network,” in Computer Modelling and Simulation (UKSim), 2012 UKSim 14th International Conference on, 2012, pp. 459–463.
[13] A. Zenati, P. Desprez, H. Razik, and S. Rael, “A methodology to assess the State of Health of lithium-ion batteries based on the battery’s parameters and a Fuzzy Logic System,” in Electric Vehicle Conference (IEVC), 2012 IEEE International, 2012, pp. 1–6.
[14] G. L. Plett, “Efficient Battery Pack State Estimation using Bar-Delta Filtering,” Int. Batter. Hybrid Fuel Cell Electr. Veh. Symp., pp. 1–8, 2009.
[15] M.A. Roscher, “Zustandserkennung von LiFePO4-Batterien für Hybrid- und Elektrofahrzeuge,” Ph.D. thesis, RWTH Aachen University, 2010.
[16] M. A. Roscher, S. Member, O. S. Bohlen, and D. U. Sauer, “Reliable State Estimation of Multicell Lithium-Ion Battery Systems,” IEEE Transactions on Energy Conversion, vol. 26, no. 3, pp. 737–743, 2011.
[17] Y. Zheng et al., “Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model,” Appl. Energy, vol. 111, no. February, pp. 571–580, 2013.
[18] X. Lin,  a G. Stefanopoulou, Y. Li, and R. D. Anderson, “State of charge estimation of cells in series connection by using only the total voltage measurement,” Am. Control Conf. (ACC), 2013, no. Ccm, pp. 704–709, 2013.
[19] T. Feng, L. Yang, X. Zhao, H. Zhang, and J. Qiang, “Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction,” J. Power Sources, vol. 281, pp. 192–203, 2015.
[20] F. Sun and R. Xiong, “A novel dual-scale cell state-of-charge estimation approach for series-connected battery pack used in electric vehicles,” J. Power Sources, vol. 274, pp. 582–594, 2015.