|Table of Contents|

Prediction of SOH of lithium batteries based onsupervised kernel self-organizing map(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2020年01期
Page:
61-66
Research Field:
Publishing date:

Info

Title:
Prediction of SOH of lithium batteries based onsupervised kernel self-organizing map
Author(s):
Zhang DaihuaZhang JianxiangBi XinghaiDai ZhongZhang Xi
Information Center,The Second Affiliated Hospital of Nanjing Medical University,Nanjing 210011,China
Keywords:
self-organizing map supervised learning supervised kernel self-organizing map state of health
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2020.44.01.010
Abstract:
The accurate and effective prediction of the State of Health(SOH)for a lithium-ion battery is especially useful for ensuring its safety and reliability and improving maintenance efficiency. A new prediction method based on supervised kernel self-organizing map(SKSOM)is proposed to predict the SOH of lithium-ion battery. First,we preprocess and normalize the raw discharge data of lithium-ion batteries; then,we design and optimize the discriminative features for SKSOM,based on which SOH prediction model is trained; finally,rigorous computational experiments on the National Aeronautics and Space Administration(NASA)lithium-ion battery dataset demonstrate the efficacy of the proposed method,which outperforms several other existing SOH predictors.

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Last Update: 2020-02-29