A merged fuzzy neural network and its applications in battery state-of-charge estimation

I. Hum Li*, Wei Yen Wang, Shun Feng Su, Yuang Shung Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

158 Citations (Scopus)


To solve learning problems with vast number of inputs, this paper proposes a novel learning structure merging a number of small fuzzy neural networks (FNNs) into a hierarchical learning structure called a merged-FNN. In this paper, the merged-FNN is proved to be a universal approximator. This computing approach uses a fusion of FNNs using B-spline membership functions (BMFs) with a reduced-form genetic algorithm (RGA). RGA is employed to tune all free parameters of the merged-FNN, including both the control points of the BMFs and the weights of the small FNNs. The merged-FNN can approximate a continuous nonlinear function to any desired degree of accuracy. For a practical application, a battery state-of-charge (BSOC) estimator, which is a twelve input, one output system, in a lithium-ion battery string is proposed to verify the effectiveness of the merged-FNN. From experimental results, the learning ability of the newly proposed merged-FNN with RGA is superior to that of the traditional neural networks with back-propagation learning.

Original languageEnglish
Pages (from-to)697-708
Number of pages12
JournalIEEE Transactions on Energy Conversion
Issue number3
Publication statusPublished - 2007 Sept


  • B-spline membership functions (BMFs)
  • Battery state-of-charge (BSOC)
  • Battery string
  • Fuzzy neural networks (FNNs)
  • Merged-FNN
  • Reduced-form genetic algorithm (RGA)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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