Modelling of a battery pack for electric vehicles using a stochastic fuzzy neural network

Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering

Volumn 223, Issue 1, 2009, Pages 27-35

  Wang, J P ^a   Xu, L ^b   Guo, J G ^a   Ding, L ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b NORTHWEST UNIVERSITY   (China)

Author keywords

Battery pack; Electric vehicles; Stochastic fuzzy neural network

Indexed keywords

AUTOMOTIVE BATTERIES; BATTERY PACK; COST FUNCTIONS; ELECTRIC VEHICLES; FUZZY INFERENCE; FUZZY LOGIC; HYDRIDES; LEARNING ALGORITHMS; LINEAR SYSTEMS; NICKEL COMPOUNDS; NICKEL METAL HYDRIDE BATTERIES; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

DYNAMIC BEHAVIOURS; FILTERING EFFECTS; NETWORK-BASED MODELING; NICKEL METAL HYDRIDE; NON-LINEAR DYNAMICS; PARAMETER LEARNING; STATES OF CHARGES; STOCHASTIC FUZZY NEURAL NETWORK;

FUZZY NEURAL NETWORKS;

EID: 67649779908     PISSN: 09544070     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544070JAUTO850     Document Type: Article

References (13)

1. Thele, M., Buller, S., Sauer, D. U., De Doncker, R. W., and Karden, E. Hybrid modeling of lead-acid batteries in frequency and time domain. J. Power Sources, 2005, 144, 461-466.
2. Shen, W. X., Chan, C. C., Lo, E. W. C., and Chau, K. T. A new battery available capacity indicator for electric vehicles using neural network. Energy Conversion Managmt, 2002, 43, 817-826.
3. Jing, Z. L., Luo, A. C. J., and Tomizuka, M. A stochastic, fuzzy, neural network for nonlinear dynamic systems. Int. J. Intell. Control Systems, 1998, 3(2), 193-203.
4. Wang, L. X. Adaptive fuzzy systems and control: design and stability analysis, 1994 (Prentice-Hall, Englewood Cliffs, New Jersey).
5. Mouzouris, G. C. and Mendel, J. M. Nonsingleton fuzzy logic systems: theory and applications. IEE Trans. Fuzzy Systems, 1997, 5, 56-69.
6. Wang, J. P. and Chen, Q. S. Stochastic fuzzy neural network and its robust parameter learning algorithm. In Advances in Neural Networks, Proceedings of the Second Infernational Symposium on Neural networks, Lecture Notes in Computer Science, vol. 3496, Chongqing, People's Republic of China, 30 May-1 June 2005, pp. 615-620 (Springer-Verlag, Berlin).
7. Gorp, J. V., Schoukens, J., and Pintelon, R. Learning neural networks with noisy inputs using the errors-in-variables approach. IEEE Trans. Neural Networks, 2000, 11(2), 402-413.
8. Plett, G. L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 1: background. J. Power Sources, 2004, 134(2), 252-261.
9. Plett, G. L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 2: modeling and identification. J. Power Sources, 2004, 134(2), 262-276.
10. Wang, J. P., Chen, Q. S., and Lin, C. T. Study on estimating of the state of charge of Ni/MH battery pack for electric vehicle. Chin. J. Mech. Engng, 2005, 41(12), 62-65.
11. Wang, J. P., Chen, Q. S., and Cao, B. G. Support vector machine based battery model for electric vehicles. Energy Conversion Managmt, 2006, 47(7), 858-864.
12. Wang, J. P., Cao, B. G., Chen, Q. S., and Wang, F. Combined state of charge estimator for electric vehicle battery pack. Control Engng Practice, 2007, 15(12), 1569-1576.
13. US Advanced Battery Consortium, USABC electric vehicle battery test procedure manual, revision 2. Report DOE/ID-10479-Rev2, US Department of Energy, Washington, DC, January 1996.