Incremental battery model using wavelet-based neural networks

IEEE Transactions on Components, Packaging and Manufacturing Technology

Volumn 1, Issue 7, 2011, Pages 1075-1081

  Song, Yujie ^a   Gao, Lijun ^a,b  

^a The Department of Mechanical Engineering   (United States)

^b BOEING COMPANY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVATION FUNCTIONS; BATTERY MODELING; COARSER APPROXIMATIONS; MODELING APPROACH; MULTI-RESOLUTION MODELING; SALIENT FEATURES; WAVELET NEURAL NETWORKS; WAVELET-BASED NEURAL NETWORK;

LITHIUM BATTERIES;

EID: 84866791165     PISSN: 21563950     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCPMT.2011.2144983     Document Type: Article

References (15)

1. P. Davis and J. Bigelow, Experiments in Multiresolution Modeling (MRM). Arlington, VA: Rand, 1998.
2. K. Schneider and O. Vasilyev, "Wavelet methods in computational fluid dynamics," Annu. Rev. Fluid Mech., vol. 42, pp. 473-503, Jan. 2010.
3. S. Wu, Y. Zhang, Z. Chen, S. Zhang, and X. Ye, "Multiresolution modeling for the feature-based parametric CAD models," in Proc. World Congr. Softw. Eng., vol. 2. Xiamen, China, May 2009, pp. 99-103.
4. V. Esfahanian, H. Darian, H. Babazadeh, M. Aghvami, R. Pasandeh, F. Torabi, and G. Ahmadi, "Numerical simulation of electrolyte particles trajectory to investigate battery cover design characteristics," J. Power Sour., vol. 191, no. 1, pp. 139-143, Jun. 2009.
5. A. A. Shah, X. Li, R. G. A. Wills, and F. C. Walsh, "A mathematical model for the soluble lead-acid flow battery," J. Electrochem. Soc., vol. 157, no. 5, pp. 589-599, 2010.
6. L. Gao, S. Liu, and R. Dougal, "Dynamic lithium-ion battery model for system simulation," IEEE Trans. Comp. Packag. Technol., vol. 25, no. 3, pp. 495-505, Sep. 2002.
7. A. Szumanowski and Y. Chang, "Battery management system based on battery nonlinear dynamics modeling," IEEE Trans. Veh. Technol., vol. 57, no. 3, pp. 1425-1432, May 2008.
8. C. Piao, X. Yang, C. Teng, and H. Yang, "An improved model based on artificial neural networks and Thevenin model for nickel metal hydride power battery," in Proc. Int. Conf. Opt., Photon. Ener. Eng., Wuhan, China, May 2010, pp. 115-118.
9. M. Sarvi and S. Adeli, "A neural network method for estimation of battery available capacity," in Proc. 45th Univ. Power Eng. Conf., Cardiff, U.K., Aug.-Sep. 2010, pp. 1-5.
10. Q. Zhang and A. Benviste, "Wavelet networks," IEEE Trans. Neural Netw., vol. 3, no. 6, pp. 889-898, Nov. 1992.
11. J. Zhang, G. G. Walter, Y. Miao, and W. Lee, "Wavelet neural networks for function learning," IEEE Trans. Signal Process., vol. 43, no. 6, pp. 1485-1497, Jun. 1995.
12. S. Yilmaz and Y. Oysal, "Fuzzy wavelet neural network models for prediction and identification of dynamical systems," IEEE Trans. Neural Netw., vol. 21, no. 10, pp. 1599-1609, Oct. 2010.
13. S. Mallat, A Wavelet Tour of Signal Processing. New York: Academic, 1998.
14. M. T. Hagan and M. B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE Trans. Neural Netw., vol. 5, no. 6, pp. 989-993, Nov. 1994.
15. R. Caruana, S. Lawrence, and C. Giles, "Overfitting in neural networks: Backpropagation, conjugate gradient, and early stopping," in Proc. Neural Inf. Process. Syst., Denver, CO, Nov. 2000, pp. 402-408.