Abstract
Consumer demand for intelligent battery charges is increasing as portable electronic applications continue to grow. Fast charging of battery packs is a problem which is difficult, and often expensive, to solve using conventional techniques. Conventional techniques only perform a linear approximation of a nonlinear behavior of a battery packs. The battery charging is a nonlinear electrochemical dynamic process and there is no exact mathematical model of battery. Better techniques are needed when a higher degree of accuracy and minimum charging time are desired. In this paper we propose soft computing approach based on fuzzy recurrent neural networks (RFNN) training by genetic algorithms to control batteries charging process. This technique does not require mathematical model of battery packs, which are often difficult, if not impossible, to obtain. Nonlinear and uncertain dynamics of the battery pack is modeled by recurrent fuzzy neural network. On base of this FRNN model, the fuzzy control rules of the control system for battery charging is generated. Computational experiments show that the suggested approach gives least charging time and least Tend-Tstart results according to the other intelligent battery charger works.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Castillo, O., Melin, P.: Soft Computing for Control of Non-Linear Dynamical Systems. Springer, Heidelberg (2001)
Ullah, M.Z., Dilip, S.: Method and Apparatus for Fast Battery Charging using Neural Network Fuzzy Logic Based Control. IEEE Aerospace and Electronic Systems Magazine 11(6), 26–34 (1996)
Ionescu, P.D., Moscalu, M., Mosclu, A.: Intelligent Charger with Fuzzy Logic. In: Int. Symp. on Signals, Circuits and Systems (2003)
Khosla, A., Kumar, S., Aggarwal, K.K.: Fuzzy Controller for Rapid Nickel-cadmium Batteries Charger through Adaptive Neuro-fuzzy Inference System (ANFIS) Architecture. In: 22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS, pp. 540–544 (2003)
Diaz, J., Martin-Ramos, J.A., Pernia, A.M., Nuno, F., Linera, F.F.: Intelligent and Universal Fast Charger for Ni-Cd and Ni-MH Batteries in Portable Applications. IEEE Trans. On Industrial Electronics 51(4), 857–863 (2004)
Jamshidi, M.: Large-Scale systems: Modeling, Control and Fuzzy Logic. Prentice Hall, Englewood Cliffs (1996)
Aliev, R.A., Aliev, R.R.: Soft Computing and Its Applications. World Scientific, Hackensack (2001)
Jamshidi, M., Krohling, R.A., dos Santos Coelho, L., Fleming, P.: Robust Control Design Using Genetic Algorithms. CRC Publishers, Boca Raton (2003)
Surmann, H.: Genetic Optimization of a Fuzzy System for Charging Batteries. IEEE Trans. on Industrial Electronics 43(5), 541–548 (1996)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Aliev, R.A., Aliev, R.R., Guirimov, B.G., Uyar, K. (2007). Recurrent Fuzzy Neural Network Based System for Battery Charging. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-72393-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
eBook Packages: Computer ScienceComputer Science (R0)