Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model
Accurate prediction of battery quality using early-cycle data is critical for battery, especially lithium battery in microgrid networks. To effectively predict the lifetime of lithium-ion batteries, a time series classification method is proposed that classifies batteries into high-lifetime and low-...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6618708 |
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| author | Zhelin Huang Fangfang Yang |
| author_facet | Zhelin Huang Fangfang Yang |
| author_sort | Zhelin Huang |
| collection | DOAJ |
| description | Accurate prediction of battery quality using early-cycle data is critical for battery, especially lithium battery in microgrid networks. To effectively predict the lifetime of lithium-ion batteries, a time series classification method is proposed that classifies batteries into high-lifetime and low-lifetime groups using features extracted from early-cycle charge-discharge data. The proposed method is based on a smooth localized complex exponential model that can extract battery features from time-frequency maps and self-adaptively select the time-frequency resolution to maximize the discrepancy of data from the two groups. A smooth localized complex exponential periodogram is then calculated to obtain the time-frequency decomposition of the whole time series data for further classification. The experimental results show that, by using battery features extracted from the first 128 charge-discharge processes, the proposed method can accurately classify batteries into high-lifetime and low-lifetime groups, with classification accuracy and specificity as high as 95.12% and 92.5%, respectively. |
| format | Article |
| id | doaj-art-9f9826b168344033a62fd166ceeed6d3 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9f9826b168344033a62fd166ceeed6d32025-02-03T01:28:23ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66187086618708Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential ModelZhelin Huang0Fangfang Yang1Department of Statistics, College of Economics, Shenzhen University, Shenzhen, ChinaSchool of Data Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, ChinaAccurate prediction of battery quality using early-cycle data is critical for battery, especially lithium battery in microgrid networks. To effectively predict the lifetime of lithium-ion batteries, a time series classification method is proposed that classifies batteries into high-lifetime and low-lifetime groups using features extracted from early-cycle charge-discharge data. The proposed method is based on a smooth localized complex exponential model that can extract battery features from time-frequency maps and self-adaptively select the time-frequency resolution to maximize the discrepancy of data from the two groups. A smooth localized complex exponential periodogram is then calculated to obtain the time-frequency decomposition of the whole time series data for further classification. The experimental results show that, by using battery features extracted from the first 128 charge-discharge processes, the proposed method can accurately classify batteries into high-lifetime and low-lifetime groups, with classification accuracy and specificity as high as 95.12% and 92.5%, respectively.http://dx.doi.org/10.1155/2021/6618708 |
| spellingShingle | Zhelin Huang Fangfang Yang Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model Complexity |
| title | Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model |
| title_full | Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model |
| title_fullStr | Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model |
| title_full_unstemmed | Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model |
| title_short | Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model |
| title_sort | quality classification of lithium battery in microgrid networks based on smooth localized complex exponential model |
| url | http://dx.doi.org/10.1155/2021/6618708 |
| work_keys_str_mv | AT zhelinhuang qualityclassificationoflithiumbatteryinmicrogridnetworksbasedonsmoothlocalizedcomplexexponentialmodel AT fangfangyang qualityclassificationoflithiumbatteryinmicrogridnetworksbasedonsmoothlocalizedcomplexexponentialmodel |