Soft computing for battery state-of-charge (BSOC) estimation in battery string systems

Yuang Shung Lee*, Wei Yen Wang, Tsung Yuan Kuo


研究成果: 雜誌貢獻期刊論文同行評審

126 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)229-239
期刊IEEE Transactions on Industrial Electronics
出版狀態已發佈 - 2008 1月

ASJC Scopus subject areas

  • 控制與系統工程
  • 電氣與電子工程


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