基于ANFIS和减法聚类的动力电池放电峰值功率预测

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Abstract Short-term peak power prediction of power battery is essential for practical use. In this paper, the adaptive neuro-fuzzy inference system(ANFIS) model is used to estimate the discharge peak power based on first order Sugeno fuzzy inference system. Temperature, SOC and ohmic resistance are selected as the inputs and 10-seconds pulse discharge peak power as the output. The training data pairs are obtained by the combination of measurements and curve fitting. Mesh generation and subtraction clustering methods are used to generate fuzzy sets. BP neural network and hybrid training methods are used to train the model based on 305 pairs of data. It found that the number of fuzzy rules is significantly reduced by using fuzzy clustering method to generate subtraction structure, the convergence rate is improved and the complexity of the model is reduced on the premise of meet the precision. Network learning by hybrid training method can strengthen the convergence ability and overcome the problem of local optimum by BP algorithm. Finally, 305 pairs of data is used to validate the model, the prediction is within 10%, ANFIS model can be well estimated the pulse peak power of the battery.

Key words： Power battery peak power adaptive neuro-fuzzy inference system subtraction clustering hybrid training

Received: 24 July 2014 Published: 13 April 2015

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	Articles by authors
	Sun Bingxiang
	Gao Ke
	Jiang Jiuchun
	Luo Min
	He Tingting
	Zheng Fangdan
	Guo Hongyu

Cite this article:

Sun Bingxiang,Gao Ke,Jiang Jiuchun等. Research on Discharge Peak Power Prediction of Battery Based on ANFIS and Subtraction Clustering[J]. Transactions of China Electrotechnical Society, 2015, 30(4): 272-280.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2015/V30/I4/272

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