Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/77345300/31940
Title: Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems
Authors: 國立臺灣師範大學電機工程學系
Y.-S. Lee
W.-Y. Wang
T.-Y. Kuo
Issue Date: 1-Jan-2008
Publisher: IEEE Industrial Electronics Society
Abstract: In this paper, a soft computing technique for estimating battery state-of-charge of individual batteries in a battery string is proposed. The soft computing approach uses a fusion of a fuzzy neural network (FNN) with B-spline membership functions (BMFs) and a reduced-form genetic algorithm (RGA). The algorithm is employed to tune both control points of the BMFs and the weights of the FNNs. The traditional multiple-input multiple-output FNN (MIMOFNN) cannot directly be used in this paper. The main reason is that there are too many free parameters in the MIMOFNN to be trained if many inputs are required. In this paper, a merged multiple-input single-output (MISO) FNN is proposed and can be trained by the RGA optimization approach. The merged MISO FNN with RGA (FNNRGA) can achieve faster convergence and lower estimation error than neural networks with the back propagation method. From experimental results, the proposed merged MISO FNNRGA is superior, more robust than the traditional method, and the overfitting suppression features are significantly improved.
URI: http://rportal.lib.ntnu.edu.tw/handle/77345300/31940
ISSN: 0278-0046
Other Identifiers: ntnulib_tp_E0604_01_018
Appears in Collections:教師著作

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