International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 3, No. 2, 8 Mar. 2011
Cover page and Table of Contents: PDF (size: 331KB)
Application of Genetic Neural Network in Power Battery Charging State-of-Charge Estimation
Full Text (PDF, 331KB), PP.24-30
Views: 0 Downloads: 0
Author(s)
Yongqin Zhou
1,*
Chao Bai
1
Jinlei Sun
1
Index Terms
BP neural network, genetic algorithm, power battery
Abstract
With global non-renewable resources and environmental issues becoming more apparent, the development of new energy vehicles have become the trend of auto industry. Hybrid vehicle becomes the key development of new energy vehicles with its long distance, low pollution, low fuel consumption characteristics and so on. The battery performances directly influence the quality of the whole vehicle performance. Considering the importance of the battery state of charge (SOC) estimation and the nonlinear relationship between the battery SOC and the external characteristic, genetic algorithm (GA) and back propagation (BP) neural network are proposed. Because of the strong global search capability of the genetic algorithm and the generalization ability of BP neural network, the hybrid vehicle Ni-MH power battery GA-BP charging model is designed. In this approach, the network training speed is superior to the traditional BP network. According to the real-time data of the batteries, the optimal solution can be concluded in a short time and with high estimation precision.
Cite This Paper
Yongqin Zhou, Chao Bai, Jinlei Sun,"Application of Genetic Neural Network in Power Battery Charging State-of-Charge Estimation", International Journal of Intelligent Systems and Applications(IJISA), vol.3, no.2, pp.24-30, 2011. DOI: 10.5815/ijisa.2011.02.04
Reference
[1] José Luis Rojo-Álvarez,Manel Martínez-Ramón, Aníbal R. Figuriras-Vidal, et al. A robust support vector algorithm for nonparametric spectral analysis [J].IEEE, 2003,10 (11):320-323
[2] John Chiasson, Baskar Vairamohan. Estimating the State of Charge of a battery [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2005, 5, 13(3).
[3] Feisi technology product research center “Neural network theory and MATLAB7 implementation”, 2005, pp.99-108.
[4] Liqun Han, “Artificial neural network tutotial”,2006,pp.58-78
[5] Yajun Wang, Xudong Wang, Yongqin Zhou, Yixin Yan, “GA-BP network based battery SOC prediction for quasi anti-damage power supply”, Electric Machines and Control, 6th, 2010, in press
[6] Xiaoping Wang, Liming Cao, “Genetic algorithms-Theory, Applications and Software”, 2002, pp.158-202.
[7] Jiansheng Wu, ‘The Back-propagation Neural Network Meteorological Forecast Model Based on Genetic Algorithms”, 2004. in press.