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Batarya Modelleri ve Şarj Durumu (SoC) Belirleme

Year 2018, Volume: 8 Issue: 1, 21 - 25, 30.06.2018

Abstract

Enerji Depolama Sistemi (EDS) araçlarından bataryalar, yenilenebilir
enerji kaynaklarının aralıklı üretimini düzeltmek ve elektrikli cihaz ve
elektrikli araçları beslemek için yaygın şekilde kullanılmaktadır. Teknik
özellikleri ve modelleme biçimlerine göre farklı batarya tipleri bulunmaktadır.
Bataryanın şarj kontrolünü sağlamak ve kullanımı sırasında kalan enerjiye göre
planlama yapabilmek için batarya şarj durumunun belirlenmesi oldukça önemlidir.
Günümüzde birçok uygulamada batarya şarj durumunu (SoC) belirlemek için farklı
yöntemler bulunmaktadır. Ancak uygulanması kolay ve anlaşılır olması nedeniyle
bu çalışmada Coulomb Sayma yöntemi kullanılmıştır. Bu
yöntem kullanılarak MATLAB programında oluşturulan batarya modeli için SoC
tahmini benzetimi ve laboratuvarda sırasıyla bataryanın şarj ve deşarj
durumları için deneyler yapılmıştır. Bu deney sonuçları kullanılarak bataryanın
şarj/deşarj karakteristikleri elde edilmiştir. Bununla birlikte, bataryanın
şarj ve deşarj işlemleri için SoC tahmini yapılmıştır.

References

  • [1] Sinkaram C., Asirvadam V. S., Nor N. B. M., “Capacity Study of lithium ion Battery for Hybrid Electrical Vehicle (HEV) A Simulation Approach”, IEEE International Conference on Signal and Irnage Processing Applications (ICSIPA), 2013.
  • [2] Erdinc, O., Vural, B., Uzunoglu, M., "A dynamic lithium-ion battery model considering the effects of temperature and capacity fading," Clean Electrical Power, 2009 International Conference, 2009, s:383-386.
  • [3] V. Spath, A. Jossen, H. Doring, and J. Garche, “The detection of the state of health of lead-acid batteries,” in Telecommunications Energy Conference, INTELEC 97, s:681–686, 1997.
  • [4] M. Dubarry, V. Svoboda, R. Hwu, and B. Liaw, “Capacity loss in rechargeable lithium cells during cycle life testing: The importance of determining state-of-charge,” Journal of Power Sources, Cilt:174, No. 2, s:1121–1125, 2007.
  • [5] F. Conte, “Battery and battery management for hybrid electric vehicles: a review,” Elektrotechnik und Informationstechnik, Cilt. 123, No. 10, s:424–431, 2006.
  • [6] J. Chiasson and B. Vairamohan "Estimating the state of charge of a battery" IEEE Trans. Control Syst. Technol., Cilt: 13, No: 3, s:465-470, 2006.
  • [7] G. L. Plett "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation" J. Power Sources, Cilt: 134, No: 2, s:277-292, 2004.
  • [8] H. W. He, R. Xiong, and H. Q. Guo, “Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles,” Applied Energy, Cilt: 89, No: 1, s:413–420, 2012.
  • [9] Z. H. Cai, G. F. Liu, and J. Luo, “Research state of charge estimation tactics of nickel-hydrogen battery,” in Proceedings of the International Symposium on Intelligence Information Processing and Trusted Computing (IPTC '10), s: 184–187, 2010.
  • [10] A. A. A. Elgammal and A. M. Sharaf, “Self-regulating particle swarm optimised controller for (photovoltaic-fuel cell) battery charging of hybrid electric vehicles,” IET Electrical Systems in Transportation, Cilt: 2, No: 2, s: 77–89, 2012.
  • [11] V. Prajapati, H. Hess, E. J. William, “A literature review of state of-charge estimation techniques applicable to lithium poly-carbon monoflouride (LI/CFx) battery,” in Proceedings of the India International Conference on Power Electronics (IICPE '10), s:1–8, 2011.
  • [12] M. R. Jonerden and B. R. Haverkort, "Which battery model to use," Software, IET, Cilt. 3, s:445-457, 2009.
  • [13] J. F. Manwell and J. G. McGowan, "Lead acid battery storage model for hybrid energy systems," Solar Energy, Cilt: 50, s:399-405, 1993.
  • [14] Bergveld H J, Kruijt W S and Notten P H L, Battery Management Systems, Design by Modelling Philips Research Book Series, 2002.
  • [15] Plett, G. L., “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 3. State and parameter estimation.” Journal of Power Sources, Cilt:134, s:277–292, 2004.
  • [16] Cheng-Hui, C., Dong, D., Zhi, Y., “Artificial Neural Network in Estimation of Battery State-of-Charge (SOC) with Non-Conventional Input Variables Selected by Correlation Analysis.” Proceedings of International Conference on Machine Learning and Cybernetics, 2002, s:1619–1625.
  • [17] Cai, C.H., Du, D., and Liu, Z.Y. “Battery State-of-Charge (SOC) Estimation Using Adaptive Neuro-Fuzzy Inference System (ANFIS).” Proceedings of the 12th IEEE International Conference on Fuzzy Systems. 2003, s: 1068–1073.
Year 2018, Volume: 8 Issue: 1, 21 - 25, 30.06.2018

Abstract

References

  • [1] Sinkaram C., Asirvadam V. S., Nor N. B. M., “Capacity Study of lithium ion Battery for Hybrid Electrical Vehicle (HEV) A Simulation Approach”, IEEE International Conference on Signal and Irnage Processing Applications (ICSIPA), 2013.
  • [2] Erdinc, O., Vural, B., Uzunoglu, M., "A dynamic lithium-ion battery model considering the effects of temperature and capacity fading," Clean Electrical Power, 2009 International Conference, 2009, s:383-386.
  • [3] V. Spath, A. Jossen, H. Doring, and J. Garche, “The detection of the state of health of lead-acid batteries,” in Telecommunications Energy Conference, INTELEC 97, s:681–686, 1997.
  • [4] M. Dubarry, V. Svoboda, R. Hwu, and B. Liaw, “Capacity loss in rechargeable lithium cells during cycle life testing: The importance of determining state-of-charge,” Journal of Power Sources, Cilt:174, No. 2, s:1121–1125, 2007.
  • [5] F. Conte, “Battery and battery management for hybrid electric vehicles: a review,” Elektrotechnik und Informationstechnik, Cilt. 123, No. 10, s:424–431, 2006.
  • [6] J. Chiasson and B. Vairamohan "Estimating the state of charge of a battery" IEEE Trans. Control Syst. Technol., Cilt: 13, No: 3, s:465-470, 2006.
  • [7] G. L. Plett "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation" J. Power Sources, Cilt: 134, No: 2, s:277-292, 2004.
  • [8] H. W. He, R. Xiong, and H. Q. Guo, “Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles,” Applied Energy, Cilt: 89, No: 1, s:413–420, 2012.
  • [9] Z. H. Cai, G. F. Liu, and J. Luo, “Research state of charge estimation tactics of nickel-hydrogen battery,” in Proceedings of the International Symposium on Intelligence Information Processing and Trusted Computing (IPTC '10), s: 184–187, 2010.
  • [10] A. A. A. Elgammal and A. M. Sharaf, “Self-regulating particle swarm optimised controller for (photovoltaic-fuel cell) battery charging of hybrid electric vehicles,” IET Electrical Systems in Transportation, Cilt: 2, No: 2, s: 77–89, 2012.
  • [11] V. Prajapati, H. Hess, E. J. William, “A literature review of state of-charge estimation techniques applicable to lithium poly-carbon monoflouride (LI/CFx) battery,” in Proceedings of the India International Conference on Power Electronics (IICPE '10), s:1–8, 2011.
  • [12] M. R. Jonerden and B. R. Haverkort, "Which battery model to use," Software, IET, Cilt. 3, s:445-457, 2009.
  • [13] J. F. Manwell and J. G. McGowan, "Lead acid battery storage model for hybrid energy systems," Solar Energy, Cilt: 50, s:399-405, 1993.
  • [14] Bergveld H J, Kruijt W S and Notten P H L, Battery Management Systems, Design by Modelling Philips Research Book Series, 2002.
  • [15] Plett, G. L., “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 3. State and parameter estimation.” Journal of Power Sources, Cilt:134, s:277–292, 2004.
  • [16] Cheng-Hui, C., Dong, D., Zhi, Y., “Artificial Neural Network in Estimation of Battery State-of-Charge (SOC) with Non-Conventional Input Variables Selected by Correlation Analysis.” Proceedings of International Conference on Machine Learning and Cybernetics, 2002, s:1619–1625.
  • [17] Cai, C.H., Du, D., and Liu, Z.Y. “Battery State-of-Charge (SOC) Estimation Using Adaptive Neuro-Fuzzy Inference System (ANFIS).” Proceedings of the 12th IEEE International Conference on Fuzzy Systems. 2003, s: 1068–1073.
There are 17 citations in total.

Details

Primary Language Turkish
Journal Section Enerjisi Dönüşümü Özel Sayısı
Authors

Efe İsa Tezde

H. İbrahim Okumuş

Publication Date June 30, 2018
Submission Date May 7, 2018
Published in Issue Year 2018 Volume: 8 Issue: 1

Cite

APA Tezde, E. İ., & Okumuş, H. İ. (2018). Batarya Modelleri ve Şarj Durumu (SoC) Belirleme. EMO Bilimsel Dergi, 8(1), 21-25.
AMA Tezde Eİ, Okumuş Hİ. Batarya Modelleri ve Şarj Durumu (SoC) Belirleme. EMO Bilimsel Dergi. June 2018;8(1):21-25.
Chicago Tezde, Efe İsa, and H. İbrahim Okumuş. “Batarya Modelleri Ve Şarj Durumu (SoC) Belirleme”. EMO Bilimsel Dergi 8, no. 1 (June 2018): 21-25.
EndNote Tezde Eİ, Okumuş Hİ (June 1, 2018) Batarya Modelleri ve Şarj Durumu (SoC) Belirleme. EMO Bilimsel Dergi 8 1 21–25.
IEEE E. İ. Tezde and H. İ. Okumuş, “Batarya Modelleri ve Şarj Durumu (SoC) Belirleme”, EMO Bilimsel Dergi, vol. 8, no. 1, pp. 21–25, 2018.
ISNAD Tezde, Efe İsa - Okumuş, H. İbrahim. “Batarya Modelleri Ve Şarj Durumu (SoC) Belirleme”. EMO Bilimsel Dergi 8/1 (June 2018), 21-25.
JAMA Tezde Eİ, Okumuş Hİ. Batarya Modelleri ve Şarj Durumu (SoC) Belirleme. EMO Bilimsel Dergi. 2018;8:21–25.
MLA Tezde, Efe İsa and H. İbrahim Okumuş. “Batarya Modelleri Ve Şarj Durumu (SoC) Belirleme”. EMO Bilimsel Dergi, vol. 8, no. 1, 2018, pp. 21-25.
Vancouver Tezde Eİ, Okumuş Hİ. Batarya Modelleri ve Şarj Durumu (SoC) Belirleme. EMO Bilimsel Dergi. 2018;8(1):21-5.

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