[1]张代华,张涧翔,毕星海,等.基于有监督核自组织映射的锂电池健康状态预测[J].南京理工大学学报(自然科学版),2020,44(01):61-66.[doi:10.14177/j.cnki.32-1397n.2020.44.01.010]
 Zhang Daihua,Zhang Jianxiang,Bi Xinghai,et al.Prediction of SOH of lithium batteries based onsupervised kernel self-organizing map[J].Journal of Nanjing University of Science and Technology,2020,44(01):61-66.[doi:10.14177/j.cnki.32-1397n.2020.44.01.010]
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基于有监督核自组织映射的锂电池健康状态预测()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年01期
页码:
61-66
栏目:
出版日期:
2020-01-30

文章信息/Info

Title:
Prediction of SOH of lithium batteries based onsupervised kernel self-organizing map
文章编号:
1005-9830(2020)01-0061-06
作者:
张代华张涧翔毕星海戴 中张 曦
南京医科大学第二附属医院 信息中心,江苏 南京 210011
Author(s):
Zhang DaihuaZhang JianxiangBi XinghaiDai ZhongZhang Xi
Information Center,The Second Affiliated Hospital of Nanjing Medical University,Nanjing 210011,China
关键词:
自组织映射 核自组织映射 有监督核自组织映射 锂电池健康状态
Keywords:
self-organizing map supervised learning supervised kernel self-organizing map state of health
分类号:
TP391
DOI:
10.14177/j.cnki.32-1397n.2020.44.01.010
摘要:
高效准确地预测锂电池的健康状态(State of health,SOH)可以保证锂电池的正常运行,提高维护效率及电池本身的稳定性。提出一种基于有监督核自组织映射(Supervised kernel self-organizing map,SKSOM)的建模方法用于锂电池SOH预测。首先,对锂电池的原始放电数据进行预处理及归一化; 然后,设计并优化了针对SKSOM的输入特征,在此基础上训练出SOH预测模型; 最后,在美国国家航空航天局(National Aeronautics and Space Administration,NASA)的标准锂电池数据集上进行验证。实验结果表明,该文所用的预测模型能有效挖掘出锂电池的SOH规律,预测性能优于其他已有的SOH预测模型。
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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备注/Memo

备注/Memo:
收稿日期:2019-09-08 修回日期:2019-12-28
基金项目:江苏省教育信息化研究课题(20180013)
作者简介:张代华(1973-),男,高级实验师,主要研究方向:数据挖掘,大数据应用,计算机网络; E-mail:zhangdaihua@just.edu.cn。
引文格式:张代华,张涧翔,毕星海,等. 基于有监督核自组织映射的锂电池健康状态预测[J]. 南京理工大学学报,2020,44(1):61-66.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-02-29