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首页> 外文期刊>Journal of Energy Storage >A fast screening framework for second-life batteries based on an improved bisecting K-means algorithm combined with fast pulse test
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A fast screening framework for second-life batteries based on an improved bisecting K-means algorithm combined with fast pulse test

机译：基于改进的分化的K均值电池的第二寿电池的快速筛选框架与快速脉冲测试相结合

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摘要

Lithium-ion batteries with high energy density have been widely used in energy storages and electrical vehicles. After retiring, they usually contain 70%-80% of their primary capacity and can still be reused for secondary applications. However, the most essential problem before such secondary usage is how to classify large amounts of retired batteries into subgroups effectively. In this paper, the retired battery screening is treated as an un-supervised clustering problem, and a fast pulse test integrated with an improved bisecting K-means algorithm has been applied to reduce the feature generation time from hours to minutes. The improved bisecting K-means algorithm generates almost the same clustering results for two groups of features: benchmark features including voltage (U), resistance (R) and capacity (Q) from conventional charge-discharge tests (-5 h), and new features from fast pulse tests (-2 mins). Thus, the proposed fast pulse test integrated with the improved bisecting K means algorithm can realize fast clustering of retired lithium-ion batteries. Finally, two open lithium-ion battery data sets from NASA and Oxford are used to demonstrate the effectiveness and accuracy of the proposed learning-based framework.
机译：高能量密度的锂离子电池已广泛用于能量储存和电气车辆。退休后，它们通常含有70％-80％的主要能力，并且仍可用于二级应用程序。但是，在此类二级使用率之前最重要的问题是如何有效地将大量退休电池分类为子组。在本文中，将退休的电池筛选被视为未经监督的聚类问题，并且已经应用​​了与改进的B分段算法集成的快速脉冲测试，以将特征生成时间从小时缩短到分钟。改进的Botecting K-Means算法对于两组特征产生几乎相同的聚类结果：基准特征包括电压（U），电阻（R）和来自传统电荷 - 放电测试（-5小时）和新的容量（Q），以及新的快速脉冲测试的功能（-2分钟）。因此，与改进的分数K表示集成的所提出的快速脉冲测试可以实现退役锂离子电池的快速聚类。最后，来自NASA和牛津的两个开放的锂离子电池数据集用于展示所提出的基于学习框架的有效性和准确性。

著录项

  • 来源
    《Journal of Energy Storage》 |2020年第10期|101739.1-101739.8|共8页
  • 作者

    Zhou Zihao; Ran Aihua; Chen Shuxiao; Zhang Xuan; Wei Guodan; Li Baohua; Kang Feiyu; Zhou Xiang; Sun Hongbin;

  • 作者单位

    Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518000 Peoples R China;

    Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518000 Peoples R China;

    Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518000 Peoples R China;

    Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518000 Peoples R China;

    Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518000 Peoples R China;

    Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518000 Peoples R China;

    Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518000 Peoples R China;

    City Univ Hong Kong Sch Data Sci Hong Kong 999077 Peoples R China|City Univ Hong Kong Dept Math Hong Kong 999077 Peoples R China;

    Tsinghua Univ Dept Elect Engn Beijing 100000 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Retired lithium-ion batteries; Secondary usage; Pulse test; Clustering method; Unsupervised learning;

    机译：退休的锂离子电池;二次使用;脉冲测试;聚类方法;无监督的学习;

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