Soft Computing for Battery State-of-Charge (BSOC) Estimation in Battery String Systems

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). Th...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2008-01, Vol.55 (1), p.229-239
Main Authors: Lee, Yuang-Shung, Wang, Wei-Yen, Kuo, Tsung-Yuan
Format: Article
Language:English
Subjects:
Algorithms
Batteries
Battery
Battery state-of-charge (BSOC)
battery string
Computer networks
Convergence
Electric batteries
Estimating
Estimation error
Fuzzy
Fuzzy control
Fuzzy neural networks
fuzzy neural networks (FNNs)
Genetic algorithms
lithium-ion battery
MIMO
Neural networks
reduced-form genetic algorithm (RGA)
Soft computing
Spline
State estimation
Strings
Studies
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Summary: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.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2007.896496