Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems
Yuang-Shung Lee
Wei-Yen Wang
Tsung-Yuan Kuo
Fu-Jen Catholic Univ., Taipei;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Jan. 2008
Volume: 55,
Issue: 1
On page(s): 229-239
ISSN: 0278-0046
INSPEC Accession Number: 9756550
Digital Object Identifier: 10.1109/TIE.2007.896496
Current Version Published: 2008-01-04
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.
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