A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm
This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extra...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-07-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/8/8/7703 |
| _version_ | 1811302439283851264 |
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| author | Xiaoyu Li Kai Song Guo Wei Rengui Lu Chunbo Zhu |
| author_facet | Xiaoyu Li Kai Song Guo Wei Rengui Lu Chunbo Zhu |
| author_sort | Xiaoyu Li |
| collection | DOAJ |
| description | This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling. |
| first_indexed | 2024-04-13T07:27:49Z |
| format | Article |
| id | doaj.art-70ffb030d4f34214922567224f087df7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T07:27:49Z |
| publishDate | 2015-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-70ffb030d4f34214922567224f087df72022-12-22T02:56:26ZengMDPI AGEnergies1996-10732015-07-01887703772810.3390/en8087703en8087703A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering AlgorithmXiaoyu Li0Kai Song1Guo Wei2Rengui Lu3Chunbo Zhu4School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaThis paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling.http://www.mdpi.com/1996-1073/8/8/7703battery groupingfractional joint Kalman filterequal-numbermodified K-means clustering |
| spellingShingle | Xiaoyu Li Kai Song Guo Wei Rengui Lu Chunbo Zhu A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm Energies battery grouping fractional joint Kalman filter equal-number modified K-means clustering |
| title | A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm |
| title_full | A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm |
| title_fullStr | A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm |
| title_full_unstemmed | A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm |
| title_short | A Novel Grouping Method for Lithium Iron Phosphate Batteries Based on a Fractional Joint Kalman Filter and a New Modified K-Means Clustering Algorithm |
| title_sort | novel grouping method for lithium iron phosphate batteries based on a fractional joint kalman filter and a new modified k means clustering algorithm |
| topic | battery grouping fractional joint Kalman filter equal-number modified K-means clustering |
| url | http://www.mdpi.com/1996-1073/8/8/7703 |
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