Next Article in Journal
A Novel Method to Magnetic Flux Linkage Optimization of Direct-Driven Surface-Mounted Permanent Magnet Synchronous Generator Based on Nonlinear Dynamic Analysis
Next Article in Special Issue
A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction
Previous Article in Journal
A Novel Strategy for Optimising Decentralised Energy Exchange for Prosumers
Previous Article in Special Issue
Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Energies 2016, 9(7), 561;

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
Author to whom correspondence should be addressed.
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)
Full-Text   |   PDF [5389 KB, uploaded 19 July 2016]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top