MAPPING OF ELECTROCHEMISTRY AND NEURAL NETWORK MODEL APPLIED IN STATE OF CHARGE ESTIMATION FOR LEAD ACID BATTERY USED IN ELECTRIC VEHICLE

https://doi.org/10.22146/ijc.21401

Bambang Sri Kaloko(1*), Soebagio Soebagio(2), Mauridhi H. Purnomo(3)

(1) Department of Electrical Engineering, Sepuluh Nopember Institute of Technology Jl. Keputih Sukolilo Surabaya
(2) Department of Electrical Engineering, Sepuluh Nopember Institute of Technology Jl. Keputih Sukolilo Surabaya
(3) Department of Electrical Engineering, Sepuluh Nopember Institute of Technology Jl. Keputih Sukolilo Surabaya
(*) Corresponding Author

Abstract


Analytical models have been developed to diminish test procedures for product realization, but they have only been partially successful in predicting the performance of battery systems consistently. The complex set of interacting physical and chemical processes within battery systems has made the development of analytical models of significant challenge. Advanced simulation tools are needed to be more accurately model battery systems which will reduce the time and cost required for product realization. As an alternative approach begun, the development of cell performance modeling using non-phenomenological models for battery systems were based on artificial neural networks (ANN) using Matlab 7.6.0(R2008b). ANN has been shown to provide a very robust and computationally efficient simulation tool for predicting state of charge for Lead Acid cells under a variety of operating conditions. In this study, the analytical model and the neural network model of lead acid battery for electric vehicle were used to determinate the battery state of charge. A precision comparison between the analytical model and the neural network model has been evaluated. The precise of the neural network model has error less than 0.00045 percent.

Keywords


Neural network; Back Propagation Network; Electrochemistry; Lead acid battery; State of Charge

Full Text:

Full Text PDF


References

[1] Jinrui, N., Fengchun, S., and Qinglian, R., 2006, A Study of Energy Management System of Electric Vehicles, IEEE Vehicle Power and Propulsion Conference, September 2006, 1–6.

[2] Yang, Y. P., and Hu, T. H., 2007, A New Energy Management System of Directly Driven Electric Vehicle with Electronic Gearshift and Regenerative Braking, American Control Conference, ACC '07, July 2007, 4419–4424.

[3] Livint, G., Horga, V., Albu, M., and Ratoi, M., 2006, Testing Possibilities of Control Algorithms for Hybrid Electric Vehicles, The 2nd WSEAS International Conference on Dynamical Systems and Control, October 2006, 47–52.

[4] Mischie and Toma, L., 2008, WSEAS Trans. Power Syst., 3, 3, 111–117.

[5] Ying, S.S., Ding, T.S., Yang, J.L., and Rong, W.H., 2008, WSEAS Trans. Power Syst., 7, 10, 1092–1103.

[6] Jung, D.Y., Lee, B.H., and Kim, S.W., 2002, J. Power Sources, 109, 1, 1–10.

[7] Lacressonniere, F., Cassoret, B., and Brudny, J., 2005, IEE Proc. Electr. Power Appl., 152, 5, 1365–1370.

[8] Robinson, R.S., 1993, J. Power Source, 42, 381–388.

[9] Pascoe, P.E., Sirisena, H., and Anbuky, A.H., 2002, Coup de Fouet Based VRLA Battery Capacity Estimation, The First IEEE International Workshop on Electronic Design, Test and Applications Proceedings, January 2002,149–153.

[10] Kiehne, H.A., 2003, Battery Technology Handbook 2nd Ed., Marcel Dekker, NY.

[11] Koehler, U., and Schmitz, C., 2001, Nickel metal hydride batteries for hybrid vehicles and new vehicle power supply systems, Proceedings of EVS.

[12] Stiegeler, M., Frey, T., Rohr, S., and Kabza, H., 2005, The bat­tery management system for lead acid battery with calibration using charge and discharge rest voltage characteristic, Proceedings of EVS.

[13] Salameh, Z.M., Casacca, M.A., and Lynch. W., 1992, IEEE Trans. Energy Convers., 7, 1, 93–98.

[14] Onishi, M., Miyata, T., Goto, H., Sonobe, H., Isogai, M., Emori, A., Kinoshita, T., and Nakanishi, M., 2001, Ni/MH battery system for HEV applications, Proceedings of EVS.

[15] Caumont, O., Moigne, P.L., Rombaut, C., Muneret, X., and Lenain, P., 2000, IEEE Trans. Energy Convers., 15, 3, 354–360.

[16] Barsali, S., and Ceraolo, M., 2002, IEEE Trans. Energy Convers., 17, 1, 16–23.

[17] Henry, A.C., Fred, F.F., and Francisco, T., 2004, J. Power Sources, 129, 1, 113–120.

[18] Chan, C.C., Lo, E.W.C., and Weixiang, S., 2000, The new calculation approach of the available capacity of batteries in electric vehicles, Proceedings of EVS 17.

[19] Singh. P., and Reisner, D., 2002, Fuzzy logic-based state of health determination of lead acid batteries, Proceedings of Telecommunications Energy Conference, 583–590.

[20] Kaloko, B.S., Purnomo, M.H., and Soebagio, Neural Network Modeling of Lead Acid Battery System, Proceeding of International Conference on Chemical Sciences, 157, Jogjakarta, October 2010.

[21] Linden, D., Handbook of Batteries, 2nd Ed., McGraw Hill, New York, 1995.

[22] Allen, J.B., and Larry, R.F., Electrochemical Method: Fundamentals and Applications 2nd Ed., Wiley India, India.



DOI: https://doi.org/10.22146/ijc.21401

Article Metrics

Abstract views : 1239 | views : 855


Copyright (c) 2011 Indonesian Journal of Chemistry

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

 

Indonesian Journal of Chemistry (ISSN 1411-9420 /e-ISSN 2460-1578) - Chemistry Department, Universitas Gadjah Mada, Indonesia.

Web Analytics View The Statistics of Indones. J. Chem.