TY - JOUR
T1 - Soft computing for battery state-of-charge (BSOC) estimation in battery string systems
AU - Lee, Yuang Shung
AU - Wang, Wei Yen
AU - Kuo, Tsung Yuan
N1 - Funding Information:
Manuscript received May 18, 2005; revised September 6, 2007. This work was supported in part by the National Science Council of Taiwan, R.O.C., under Grant NSC 91-2213-E-030-007 and in part by the MRL, ITRI of Taiwan. Y.-S. Lee is with the Graduate Institute of Applied Science and the Department of Electronic Engineering, Fu-Jen Catholic University, Taipei 24205, Taiwan, R.O.C. W.-Y. Wang is with the Department of Applied Electronics Technology, National Taiwan Normal University, Taipei 106, Taiwan, R.O.C. (e-mail: [email protected]). T.-Y. Kuo is with the Department of Electronic Engineering, Fu-Jen Catholic University, Taipei 24205, Taiwan, R.O.C. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIE.2007.896496
PY - 2008/1
Y1 - 2008/1
N2 - 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.
AB - 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.
KW - Battery state-of-charge (BSOC)
KW - Battery string
KW - Fuzzy neural networks (FNNs)
KW - Lithium-ion battery
KW - Reduced-form genetic algorithm (RGA)
KW - Soft computing
UR - http://www.scopus.com/inward/record.url?scp=38349004913&partnerID=8YFLogxK
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U2 - 10.1109/TIE.2007.896496
DO - 10.1109/TIE.2007.896496
M3 - Article
AN - SCOPUS:38349004913
SN - 0278-0046
VL - 55
SP - 229
EP - 239
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 1
ER -
