{"title":"基于最佳u形集的电池充放电特征提取方法","authors":"Jingping Chen, Yuanyuan Liu, Mingyu Gao, Zhiwei He, Zhongfei Yu","doi":"10.1109/INDIN.2018.8471940","DOIUrl":null,"url":null,"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.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"66 1","pages":"207-211"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Battery charging and discharging feature extraction method based on the best u-shapelets\",\"authors\":\"Jingping Chen, Yuanyuan Liu, Mingyu Gao, Zhiwei He, Zhongfei Yu\",\"doi\":\"10.1109/INDIN.2018.8471940\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":6467,\"journal\":{\"name\":\"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)\",\"volume\":\"66 1\",\"pages\":\"207-211\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDIN.2018.8471940\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN.2018.8471940","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Battery charging and discharging feature extraction method based on the best u-shapelets
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.