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Battery Aging Detection Based on Sequential Clustering and Similarity Analysis
Yamaguchi University, Yamaguchi, Japan.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
Volvo Group Trucks Technology, Göteborg, Sweden.
2012 (English)In: IS'2012: 2012 6th IEEE International Conference Intelligent Systems, Proceedings, Piscataway, N.J.: IEEE Press, 2012, 42-47 p., 6335112Conference paper, Published paper (Refereed)
Abstract [en]

The battery cells are an important part of electric and hybrid vehicles and their deterioration due to aging directly affects the life cycle and performance of the whole battery system. Therefore an early aging detection of the battery cell is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for battery aging detection, based on available data chunks from real operation of the vehicle. The first step is to aggregate (reduce) the original large amount of data by much smaller number of cluster centers. This is done by a newly proposed sequential clustering algorithm that arranges the clusters in decreasing order of their volumes. The next step is the proposed fuzzy inference procedure for weighed approximation of the cluster centers that creates comparable one dimensional fuzzy model for each available data set. Finally, the detection of the aged battery is treated as a similarity analysis problem, in which the pair distances between all battery cells are estimated by analyzing the predicted values from the respective fuzzy models. All these three steps of the computational procedure are explained in the paper and applied to real experimental data for battery aging detection. The results are positive and suggestions for further improvements are made in the conclusions. © 2012 IEEE.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2012. 42-47 p., 6335112
Keyword [en]
battery aging detection, data aggregation, fuzzy inference, sequential clustering, similarity analysis, weighted approximation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-19512DOI: 10.1109/IS.2012.6335112Scopus ID: 2-s2.0-84869838414ISBN: 978-1-4673-2278-2 (electronic)ISBN: 978-1-4673-2276-8 (print)ISBN: 978-1-4673-2277-5 (electronic)ISBN: 978-1-4673-2276-8 (print)OAI: oai:DiVA.org:hh-19512DiVA: diva2:550673
Conference
6th IEEE International Conference Intelligent Systems, IS 2012, Sofia, Bulgaria, 6-8 September, 2012
Available from: 2012-09-07 Created: 2012-09-07 Last updated: 2017-04-24Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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