Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm

Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm

Following the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists and engineers. During the lifetime of the power lithium ion battery, SOF r...

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Bibliographic Details
Main Authors: Dasong Wang, Feng Yang, Lin Gan, Yuliang Li
Format: Article
Language:English
Published: MDPI AG 2019-01-01
Series:World Electric Vehicle Journal
Subjects:
power battery
SOF
fuzzy prediction
fuzzy c-means clustering
Online Access:http://www.mdpi.com/2032-6653/10/1/1
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spelling doaj-a31f99a683c54563aaf9b0b95dcdd7fb2020-11-24T21:46:35ZengMDPI AGWorld Electric Vehicle Journal2032-66532019-01-01101110.3390/wevj10010001wevj10010001Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering AlgorithmDasong Wang0Feng Yang1Lin Gan2Yuliang Li3University of Electronic Science and Technology of China, Qingshuihe Campus of UESTC, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, ChinaUniversity of Electronic Science and Technology of China, Qingshuihe Campus of UESTC, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, ChinaUniversity of Electronic Science and Technology of China, Qingshuihe Campus of UESTC, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, ChinaUniversity of Electronic Science and Technology of China, Qingshuihe Campus of UESTC, No.2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, ChinaFollowing the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists and engineers. During the lifetime of the power lithium ion battery, SOF reflects the maximum instantaneous output power of the battery. When discarded, it is able to show the degree of performance degradation of the power battery when also taken as a performance evaluation parameter. In this paper, the variables closely related to SOF have been selected to conduct the fuzzy inference system, which is optimized by the fuzzy c-means clustering algorithm, to estimate the SOF of the power lithium ion battery, whose relations can be proved by experimental data. Our simulation results and experimental results demonstrate the feasibility and advantages of the estimation strategy.http://www.mdpi.com/2032-6653/10/1/1power batterySOFfuzzy predictionfuzzy c-means clustering
collection DOAJ
language English
format Article
sources DOAJ
author Dasong Wang
Feng Yang
Lin Gan
Yuliang Li
spellingShingle Dasong Wang
Feng Yang
Lin Gan
Yuliang Li
Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm
World Electric Vehicle Journal
power battery
SOF
fuzzy prediction
fuzzy c-means clustering
author_facet Dasong Wang
Feng Yang
Lin Gan
Yuliang Li
author_sort Dasong Wang
title Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm
title_short Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm
title_full Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm
title_fullStr Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm
title_full_unstemmed Fuzzy Prediction of Power Lithium Ion Battery State of Function Based on the Fuzzy c-Means Clustering Algorithm
title_sort fuzzy prediction of power lithium ion battery state of function based on the fuzzy c-means clustering algorithm
publisher MDPI AG
series World Electric Vehicle Journal
issn 2032-6653
publishDate 2019-01-01
description Following the widespread and large-scale application of power lithium ion battery, State of Function (SOF) estimation technology of power lithium ion batteries has gained an increasing amount of attention from both scientists and engineers. During the lifetime of the power lithium ion battery, SOF reflects the maximum instantaneous output power of the battery. When discarded, it is able to show the degree of performance degradation of the power battery when also taken as a performance evaluation parameter. In this paper, the variables closely related to SOF have been selected to conduct the fuzzy inference system, which is optimized by the fuzzy c-means clustering algorithm, to estimate the SOF of the power lithium ion battery, whose relations can be proved by experimental data. Our simulation results and experimental results demonstrate the feasibility and advantages of the estimation strategy.
topic power battery
SOF
fuzzy prediction
fuzzy c-means clustering
url http://www.mdpi.com/2032-6653/10/1/1
work_keys_str_mv AT dasongwang fuzzypredictionofpowerlithiumionbatterystateoffunctionbasedonthefuzzycmeansclusteringalgorithm
AT fengyang fuzzypredictionofpowerlithiumionbatterystateoffunctionbasedonthefuzzycmeansclusteringalgorithm
AT lingan fuzzypredictionofpowerlithiumionbatterystateoffunctionbasedonthefuzzycmeansclusteringalgorithm
AT yuliangli fuzzypredictionofpowerlithiumionbatterystateoffunctionbasedonthefuzzycmeansclusteringalgorithm
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