Battery charging and discharging feature extraction method based on the best u-shapelets

Jingping Chen, Yuanyuan Liu, Mingyu Gao, Zhiwei He, Zhongfei Yu
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Abstract

With the advancement of science and technology,batteries have become an indispensable item in our daily life. At the same time, the study of the charge-discharging curve of the battery plays an important role. The problem of battery charging and discharging curve can be regarded as a time series data mining problem. We utilize the unsupervised shape u-shapelets for time series data mining, which is a newly emerging tiny local feature that has been widely used in many fields, e.g., battery grouping. Experimental results show the practicability and effectiveness of the battery charge/discharge feature extraction method using the best u-shapelets, the ability of the local characteristics of u-shapelets to provide more insights for the data, and the sensitivity to irrelevant data in the charging and discharging curve of the battery is reduced. Extracting local feature u-shapelets from battery charging and discharging curves is helpful for battery grouping.
基于最佳u形集的电池充放电特征提取方法
随着科技的进步,电池已经成为我们日常生活中不可缺少的物品。同时,对电池充放电曲线的研究也起着重要的作用。电池充放电曲线问题可以看作是一个时间序列数据挖掘问题。我们利用无监督形状u-shapelets进行时间序列数据挖掘,这是一种新兴的微小局部特征,已广泛应用于许多领域,如电池分组。实验结果表明,采用最佳u形块的电池充放电特征提取方法具有实用性和有效性,u形块的局部特征能够为数据提供更多的洞见,降低了电池充放电曲线中对无关数据的敏感性。从电池充放电曲线中提取局部特征u-shapelets有助于电池分组。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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