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Yıl 2020, Cilt: 7 Sayı: 3, 88 - 94, 05.10.2020

Yusuf Karabacak İlker Ali Ozkan İsmail Saritas

https://doi.org/10.31593/ijeat.778689
Cited By: 1

Öz

Kaynakça

  • Chen, M., Member, S., Rinc, G. A. 2006. Accurate Electrical Battery Model Capable of Predicting Runtime and I – V Performance. IEEE Transactıons On Energy Conversion, 21 (2), 504–511.
  • Low, W. Y., Aziz, J. A., Idris, N. R. N., Saidur, R. 2013. Electrical model to predict current-voltage behaviours of lithium ferro phosphate batteries using a transient response correction method. Journal of Power Sources, 221, 201–209.
  • Knauff, M. C., Dafis, C. J., Niebur, D., Kwatny, H. G., Nwankpa, C. O., Metzer, J. 2007. Simulink model for hybrid power system test-bed. IEEE Electric Ship Technologies Symposium ESTS 2007, 421–427.
  • Plett, G. L. 2004. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background, Journal of Power Sources, 134(2), 252–261.
  • Grasberger, C., Dolan, D. S., Taufik, T. 2012. Development of an Open-Source High-Performance Battery Management System. North American Power Symposium (NAPS), 1–3.
  • Kaiser, R. 2007. Optimized battery-management system to improve storage lifetime in renewable energy systems, Journal of Power Sources, 168(1 SPEC. ISS.), 58–65.
  • Gotaas, E. and Nettum, A. 2000. Single cell battery management systems (BMS). INTELEC Twenty-Second International Telecommunications Energy Conference, 2(36), 695–702.
  • Jung, D. Y., Lee, B. H., Kim, S. W. 2002. Development of battery management system for nickel-metal hydride batteries in electric vehicle applications. Journal of Power Sources. 109(1), 1–10.
  • Zhu, W. 2011. "A Smart Battery Management System for Large Format Lithium Ion Cells", Ph.D. thesis, The University of Toledo Graduate Faculty, Toledo, iii–iv.
  • Wan, X. W. X., Wu, J. W. J., Hu, H. H. H. 2009. The smart Battery management system. International Conference on Test and Measurement ICTM ’09., 129–132.
  • Sitterly, M., Member, S., Wang, L. Y., Member, S., Yin, G. G., Wang, C. 2011. Enhanced Identi fi cation of Battery Models for Real-Time Battery Management. IEEE Transactions on Sustainable Energy, 2(3),300–308.
  • Soylu, E., Bayir, R. 2016. Measurement of Electrical Conditions of Rechargeable Batteries, Measurement and Control, 49(2), 72–81.
  • Özkan, İ.A., Ciniviz, M., Candan, F. 2015. Estimating Engine Performance and Emission Values Using ANFIS. International Journal of Automotive Engineering and Technologies, 4(1), 63-67.
  • Karabacak, Y., Uysal, A. 2017. "Fuzzy logic controlled brushless direct current motor drive design and application for regenerative braking." 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 1-7.
  • Karabacak, Y., Uysal, A. 2020. "An Embedded Controller Application with Regenerative Braking for the Electric Vehicle." Elektronika ir Elektrotechnika 26.1: 10-17.
  • Singh, P., Reisner, D. 2002. Fuzzy logic-based state-of-health determination of lead acid batteries. Telecommunications Energy Conference, 2002. INTELEC. 24th Annual International. IEEE, 583– 590.
  • Huria, T., Ceraolo, M., Gazzarri, J., Jackey, R. 2012. High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells. 2012 IEEE International Electric Vehicle Conference, 1–8.
  • Salkind, A. J., Fennie, C., Singh, P., Atwater, T., Reisner, D. E. 1999. Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology. Journal of Power Sources, 80 (1), 293–300.
  • Marcia, G., A Battery's Guide to Immortality, 2019. https://www.mtu.edu/magazine/research/2019/stories/battery-immortality/. (08 August 2019).
  • Tremblay, O., Dessaint, L. A. 2009. Experimental validation of a battery dynamic model for EV applications. World Electric Vehicle Journal, 3(1), 289–298.
  • Cheng, K. W. E., Divakar, B. P., Wu, H., Ding, K., Ho, H. F. 2011. Battery-management system (BMS) and SOC development for electrical vehicles, Vehicular Technology, IEEE Transactions on, 60(1), 76–88.
  • Jang, J.-S. R. 1993, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on Systems Man and Cybernetics, 23(3), 665-685.
  • Şen, Z. Mühendislikte Bulanık Mantık (Fuzzy) ile Modelleme Prensipleri. Su Vakfı Yayınları, İstanbul, 2004.
  • Cojbasic, Z.V., Nikolic Ć., Cojbasic I.L. 2011. Computationally Intelligent Modelling and Control of Fluidized Bed Combustion Process. Thermal Science,15,321-338.

Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS)

Yıl 2020, Cilt: 7 Sayı: 3, 88 - 94, 05.10.2020

Yusuf Karabacak İlker Ali Ozkan İsmail Saritas

https://doi.org/10.31593/ijeat.778689
Cited By: 1

Öz

Thanks to their electrochemical structure, batteries are the elements that can store electrical energy and spend on a load when the electrical energy they store is needed. Today, with the widespread use of electrically powered mobile devices, rechargeable batteries have become widespread and battery technologies have developed. With the idea that the latest technology systems and electric vehicles will become widespread in the future, the studies on batteries are increasing day by day. In this study, charge state estimation of Li-ion battery cell used to provide power in many applications was realized by using adaptive neural fuzzy inference system (ANFIS). A Li-ion battery was discharged using variable electrical loads with a battery discharge circuit modeled on MATLAB Simulink and current, voltage, temperature and current power parameters of the battery were selected as input variables. Battery parameters and charge status data obtained from discharge tests using different electrical loads on MATLAB Simulink were used as training and test parameters of neural network. Using the MATLAB ANFIS toolbox, the system was trained with 80% of the battery parameters obtained in the battery discharge experiments and with 20% as testing data, the success performance was interpreted by applying the adaptive neural fuzzy inference system.

Anahtar Kelimeler

ANFIS Battery charge status Charge status Estimation Rechargeable batteries

Kaynakça

  • Chen, M., Member, S., Rinc, G. A. 2006. Accurate Electrical Battery Model Capable of Predicting Runtime and I – V Performance. IEEE Transactıons On Energy Conversion, 21 (2), 504–511.
  • Low, W. Y., Aziz, J. A., Idris, N. R. N., Saidur, R. 2013. Electrical model to predict current-voltage behaviours of lithium ferro phosphate batteries using a transient response correction method. Journal of Power Sources, 221, 201–209.
  • Knauff, M. C., Dafis, C. J., Niebur, D., Kwatny, H. G., Nwankpa, C. O., Metzer, J. 2007. Simulink model for hybrid power system test-bed. IEEE Electric Ship Technologies Symposium ESTS 2007, 421–427.
  • Plett, G. L. 2004. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background, Journal of Power Sources, 134(2), 252–261.
  • Grasberger, C., Dolan, D. S., Taufik, T. 2012. Development of an Open-Source High-Performance Battery Management System. North American Power Symposium (NAPS), 1–3.
  • Kaiser, R. 2007. Optimized battery-management system to improve storage lifetime in renewable energy systems, Journal of Power Sources, 168(1 SPEC. ISS.), 58–65.
  • Gotaas, E. and Nettum, A. 2000. Single cell battery management systems (BMS). INTELEC Twenty-Second International Telecommunications Energy Conference, 2(36), 695–702.
  • Jung, D. Y., Lee, B. H., Kim, S. W. 2002. Development of battery management system for nickel-metal hydride batteries in electric vehicle applications. Journal of Power Sources. 109(1), 1–10.
  • Zhu, W. 2011. "A Smart Battery Management System for Large Format Lithium Ion Cells", Ph.D. thesis, The University of Toledo Graduate Faculty, Toledo, iii–iv.
  • Wan, X. W. X., Wu, J. W. J., Hu, H. H. H. 2009. The smart Battery management system. International Conference on Test and Measurement ICTM ’09., 129–132.
  • Sitterly, M., Member, S., Wang, L. Y., Member, S., Yin, G. G., Wang, C. 2011. Enhanced Identi fi cation of Battery Models for Real-Time Battery Management. IEEE Transactions on Sustainable Energy, 2(3),300–308.
  • Soylu, E., Bayir, R. 2016. Measurement of Electrical Conditions of Rechargeable Batteries, Measurement and Control, 49(2), 72–81.
  • Özkan, İ.A., Ciniviz, M., Candan, F. 2015. Estimating Engine Performance and Emission Values Using ANFIS. International Journal of Automotive Engineering and Technologies, 4(1), 63-67.
  • Karabacak, Y., Uysal, A. 2017. "Fuzzy logic controlled brushless direct current motor drive design and application for regenerative braking." 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 1-7.
  • Karabacak, Y., Uysal, A. 2020. "An Embedded Controller Application with Regenerative Braking for the Electric Vehicle." Elektronika ir Elektrotechnika 26.1: 10-17.
  • Singh, P., Reisner, D. 2002. Fuzzy logic-based state-of-health determination of lead acid batteries. Telecommunications Energy Conference, 2002. INTELEC. 24th Annual International. IEEE, 583– 590.
  • Huria, T., Ceraolo, M., Gazzarri, J., Jackey, R. 2012. High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells. 2012 IEEE International Electric Vehicle Conference, 1–8.
  • Salkind, A. J., Fennie, C., Singh, P., Atwater, T., Reisner, D. E. 1999. Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology. Journal of Power Sources, 80 (1), 293–300.
  • Marcia, G., A Battery's Guide to Immortality, 2019. https://www.mtu.edu/magazine/research/2019/stories/battery-immortality/. (08 August 2019).
  • Tremblay, O., Dessaint, L. A. 2009. Experimental validation of a battery dynamic model for EV applications. World Electric Vehicle Journal, 3(1), 289–298.
  • Cheng, K. W. E., Divakar, B. P., Wu, H., Ding, K., Ho, H. F. 2011. Battery-management system (BMS) and SOC development for electrical vehicles, Vehicular Technology, IEEE Transactions on, 60(1), 76–88.
  • Jang, J.-S. R. 1993, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on Systems Man and Cybernetics, 23(3), 665-685.
  • Şen, Z. Mühendislikte Bulanık Mantık (Fuzzy) ile Modelleme Prensipleri. Su Vakfı Yayınları, İstanbul, 2004.
  • Cojbasic, Z.V., Nikolic Ć., Cojbasic I.L. 2011. Computationally Intelligent Modelling and Control of Fluidized Bed Combustion Process. Thermal Science,15,321-338.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliği
Bölüm Research Article
Yazarlar

Yusuf Karabacak 0000-0001-9864-7512

İlker Ali Ozkan 0000-0002-5715-1040

İsmail Saritas 0000-0002-5743-4593

Yayımlanma Tarihi 5 Ekim 2020
Gönderilme Tarihi 10 Ağustos 2020
Kabul Tarihi 29 Eylül 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 7 Sayı: 3

Kaynak Göster

APA Karabacak, Y., Ozkan, İ. A., & Saritas, İ. (2020). Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS). International Journal of Energy Applications and Technologies, 7(3), 88-94. https://doi.org/10.31593/ijeat.778689
AMA Karabacak Y, Ozkan İA, Saritas İ. Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS). IJEAT. Ekim 2020;7(3):88-94. doi:10.31593/ijeat.778689
Chicago Karabacak, Yusuf, İlker Ali Ozkan, ve İsmail Saritas. “Estimation of Li-Ion Battery State of Charge Using Adaptive Neural Fuzzy Inference System (ANFIS)”. International Journal of Energy Applications and Technologies 7, sy. 3 (Ekim 2020): 88-94. https://doi.org/10.31593/ijeat.778689.
EndNote Karabacak Y, Ozkan İA, Saritas İ (01 Ekim 2020) Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS). International Journal of Energy Applications and Technologies 7 3 88–94.
IEEE Y. Karabacak, İ. A. Ozkan, ve İ. Saritas, “Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS)”, IJEAT, c. 7, sy. 3, ss. 88–94, 2020, doi: 10.31593/ijeat.778689.
ISNAD Karabacak, Yusuf vd. “Estimation of Li-Ion Battery State of Charge Using Adaptive Neural Fuzzy Inference System (ANFIS)”. International Journal of Energy Applications and Technologies 7/3 (Ekim 2020), 88-94. https://doi.org/10.31593/ijeat.778689.
JAMA Karabacak Y, Ozkan İA, Saritas İ. Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS). IJEAT. 2020;7:88–94.
MLA Karabacak, Yusuf vd. “Estimation of Li-Ion Battery State of Charge Using Adaptive Neural Fuzzy Inference System (ANFIS)”. International Journal of Energy Applications and Technologies, c. 7, sy. 3, 2020, ss. 88-94, doi:10.31593/ijeat.778689.
Vancouver Karabacak Y, Ozkan İA, Saritas İ. Estimation of li-ion battery state of charge using adaptive neural fuzzy inference system (ANFIS). IJEAT. 2020;7(3):88-94.

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