Battery state-of-charge (SOC) estimation using adaptive neuro-fuzzy inference system (ANFIS)

IEEE International Conference on Fuzzy Systems

Volumn 2, Issue , 2003, Pages 1068-1073

  Cai, C H ^a   Du, D ^a   Liu, Z Y ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Adaptive neuro fuzzy inference system (ANFIS); Artificial neural network (ANN); Battery; State of charge (SOC)

Indexed keywords

BACKPROPAGATION; CORRELATION METHODS; FEEDFORWARD NEURAL NETWORKS; FUZZY SETS; INFERENCE ENGINES; LARGE SCALE SYSTEMS;

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS);

NONLINEAR SYSTEMS;

EID: 0037523276     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. D.U. Sauer, G. Bopp, A. Jossen, et al., State-of-charge - what do we really speak about? INTELEC, 1999.
2. S. Piller, Marion Perrin, A. Jossen. Methods for state-of-charge determination and their application. Journal of Power Sources, 96(2001) 113-120.
3. th European Photovoltaic Solar Energy Conference, Glasgow, 2000.
4. th Annual Battery Conference on Applications and Advances (CAT. No. 00TH8490), IEEE, 1999, pp. 173-7. Piscataway, NJ, USA.
5. J.-S. Roger Jang. ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Trans. on Systems, Man, and Cybernetics, 23(03): 665-685, May 1993.
6. W.X. Shen, C.C. Chan, E.W.C. Lo, K.T. Chau. Adaptive Neuro-Fuzzy Modeling of Battery Residual Capacity for Electric Vehicles. IEEE Trans. on Industrial Electronics, 49(3): 677-684, June, 2002.
7. Pascoe Phillip E., Anbuky, Adnan H. Adaptive fuzzy coup de fouet based VRLA battery capacity estimation. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, pp. 2157-2162, Oct. 2001.
8. A.G. Bahbah, A.A. Girgis, Input feature selection for real-time transient stability assessment for artificial neural network (ANN) using ANN sensitivity analysis, IEEE International Conference on Power Industry Computer Applications, pp. 295-300, 1999.
9. C.C. Peck, A.P. Dhawan, C.M. Meyer, Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring, IEEE International Conference on Neural Networks, vol.2, pp.1115 -1122, 1993.
10. Z. Guo, R.E. Uhrig, Using genetic algorithms to select inputs for neural networks, International Workshop on Combinations of Genetic Algorithms and Neural Networks, pp.223-224, 1992.
11. I. Drezga, S. Rahman. Input variable selection for ANN-based short-term load forecasting. IEEE Trans. on Power System, 13(4), pp. 1238-1244, Nov. 1998.
12. K.K. Lee, Y.S. Xu, Input reduction in human sensation modeling using independent component analysis, IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol.4, pp.1854 -1859, 2001.
13. C.H. Cai, D. Du, et al. Artificial neural network in estimation of battery state-of-charge (SOC) with nonconventional input variables selected by correlation analysis. The First IEEE International Conference on Machine Learning and Cybernetics 2002 (ICMLC'02), Beijing, pp. 1619-1625, Nov. 4-5 2002.
14. T. Takagi and M. Sugeno. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. on Systems, Man, and Cybernetics, 15:116-132, 1985.
15. M. Sugeno and G. T.Kang, Structure identification of fuzzy model. Fuzzy Sets and Systems, 28:15-33, 1988.
16. C.H. Cai, D. Du, et al. State-of-charge (SOC) estimation of high power Ni-MH rechargeable battery with artificial neural network. 9th International Conference on Neural Information Processing (ICONIP'02), Singapore, Vol.2, pp.824-828, Nov. 18-22, 2002.