SOC Prediction Research of VRB Based on Elman Neural Network

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As an important part of the micro-grid, the energy storage devices are playing an important role to stabilize power and energy fluctuations of micro-grid system, improve the stability and schedulability of the system and the quality of electricity. All-Vanadium Redox Flow Battery (VRB) has been more and more used because of its advantages on environmentally friendly, long cycle life, safety and reliability, achieving economies of scale energy storage and so on. State of Charge (SOC) prediction is an important part to the Power Control System (PCS) and Energy Management System (EMS) of the energy storage devices. This paper established a SOC prediction model of VRB based on Elman Neural Network because Neural Network has the characteristics of non-linear and self-learning to simulate external characteristic of VRB which is a highly non-linear system. Then we trained the Elman Neural Network with the experimental data. The result shows that this method has a high accuracy. By contrast with the BP neural network, the prediction is better than Back Propagation (BP)Neural Network.

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Edited by:

Prof. Kyle Jiang, Prof. Shinn-Liang Chang and Prof. Ruxu Du

Pages:

118-122

Citation:

H. X. Han et al., "SOC Prediction Research of VRB Based on Elman Neural Network", Applied Mechanics and Materials, Vol. 826, pp. 118-122, 2016

Online since:

February 2016

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$38.00

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[1] C.S. Wang. Analysis and Simulation of Microgrids [M]. Beijing: Science Press, (2013).

[2] Y. Brunet. Energy Storage [M]. Beijing: China Machine Press, (2013).

[3] S.B. Frank, G.L. Jonah. Large energy storage systems handbook [M]. China Machine Press, (2013).

[4] G L Plett. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background[J], Journal of Power Sources, 2004, 134(2): 252~261.

DOI: https://doi.org/10.1016/j.jpowsour.2004.02.031

[5] G L Plett. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2. Modeling and identification[J]. Journal of Power Sources, 2004(2), 134: 262-276.

DOI: https://doi.org/10.1016/j.jpowsour.2004.02.032

[6] G L Plett. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 3. State and parameter estimation[J]. Journal of Power sources, 2004, 134(2): 277-292.

DOI: https://doi.org/10.1016/j.jpowsour.2004.02.033

[7] A.D. Yin, W.X. Zhang, H. Jiang. Research on estimation for SOC of LiFePO_4 Li-ion battery based on neural network[J]. Journal of Electronic Measurement & Instrument, 2011, 25(5): 433-437.

DOI: https://doi.org/10.3724/sp.j.1187.2011.00433

[8] X.Z. Gao, X.M. Gao, S.J. Ovaska. A modified Elman neural network model with application to dynamical systems identification[C]/ Systems, Man, and Cybernetics, 1996., IEEE International Conference on. IEEE, 1996: 1376-1381 vol. 2.

DOI: https://doi.org/10.1109/icsmc.1996.571312

[9] Q. Zhang. Prediction of Data from Pollution Sources Based on Elman Neural Network[J]. Journal of South China University of Technology, 2009, 37(5): 135-138.

[10] C. Blanc, A. Rufer. Multiphysics and energetic modeling of a vanadium redox flow battery[C]/ 2008 IEEE International Conference on Sustainable Energy Technologies. 2008: 696 - 701.

DOI: https://doi.org/10.1109/icset.2008.4747096