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

Year 2020, Volume: 7 Issue: 3, 88 - 94, 05.10.2020

Yusuf Karabacak , İlker Ali Ozkan , İsmail Saritas

https://doi.org/10.31593/ijeat.778689

Cited By: 1

Abstract

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.

Keywords

ANFIS, Battery charge status, Charge status, Estimation, Rechargeable batteries

References

• 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.