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Energies 2016, 9(7), 561; doi:10.3390/en9070561

Battery Grouping with Time Series Clustering Based on Affinity Propagation

Department of Electronic & Information, Hangzhou Dianzi University, 2nd Street, Xiasha Higher Education Zone, Hangzhou 310018, China
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Academic Editors: José C. Riquelme, Alicia Troncoso and Francisco Martínez-Álvarez
Received: 11 April 2016 / Revised: 19 June 2016 / Accepted: 12 July 2016 / Published: 19 July 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
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Abstract

Battery grouping is a technology widely used to improve the performance of battery packs. In this paper, we propose a time series clustering based battery grouping method. The proposed method utilizes the whole battery charge/discharge sequence for battery grouping. The time sequences are first denoised with a wavelet denoising technique. The similarity matrix is then computed with the dynamic time warping distance, and finally the time series are clustered with the affinity propagation algorithm according to the calculated similarity matrices. The silhouette index is utilized for assessing the performance of the proposed battery grouping method. Test results show that the proposed battery grouping method is effective. View Full-Text
Keywords: battery grouping; affinity propagation; time series clustering; wavelet denoising battery grouping; affinity propagation; time series clustering; wavelet denoising
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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He, Z.; Gao, M.; Ma, G.; Liu, Y.; Tang, L. Battery Grouping with Time Series Clustering Based on Affinity Propagation. Energies 2016, 9, 561.

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