A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm

This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extra...

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Veröffentlicht in:	Energies (Basel) Jg. 8; H. 8; S. 7703 - 7728
Hauptverfasser:	Li, Xiaoyu, Song, Kai, Wei, Guo, Lu, Rengui, Zhu, Chunbo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.08.2015
Schlagworte:	Algorithms battery grouping Clustering Electric batteries Electric vehicles Electrochemical impedance spectroscopy equal-number fractional joint Kalman filter Heat Kalman filters Lithium Mathematical models modified K-means clustering Phosphates Spectrum analysis Vector quantization
ISSN:	1996-1073, 1996-1073
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Abstract	This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling.
AbstractList	This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling.
Author	Lu, Rengui Song, Kai Zhu, Chunbo Li, Xiaoyu Wei, Guo
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SubjectTerms	Algorithms battery grouping Clustering Electric batteries Electric vehicles Electrochemical impedance spectroscopy equal-number fractional joint Kalman filter Heat Kalman filters Lithium Mathematical models modified K-means clustering Phosphates Spectrum analysis Vector quantization
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Title	A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm
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