Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition
This study introduces visual cognition into Lithium-ion battery capacity estimation. The proposed method consists of four steps. First, the acquired charging current or discharge voltage data in each cycle are arranged to form a two-dimensional image. Second, the generated image is decomposed into m...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/6342170 |
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| _version_ | 1832556955842904064 |
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| author | Yujie Cheng Laifa Tao Chao Yang |
| author_facet | Yujie Cheng Laifa Tao Chao Yang |
| author_sort | Yujie Cheng |
| collection | DOAJ |
| description | This study introduces visual cognition into Lithium-ion battery capacity estimation. The proposed method consists of four steps. First, the acquired charging current or discharge voltage data in each cycle are arranged to form a two-dimensional image. Second, the generated image is decomposed into multiple spatial-frequency channels with a set of orientation subbands by using non-subsampled contourlet transform (NSCT). NSCT imitates the multichannel characteristic of the human visual system (HVS) that provides multiresolution, localization, directionality, and shift invariance. Third, several time-domain indicators of the NSCT coefficients are extracted to form an initial high-dimensional feature vector. Similarly, inspired by the HVS manifold sensing characteristic, the Laplacian eigenmap manifold learning method, which is considered to reveal the evolutionary law of battery performance degradation within a low-dimensional intrinsic manifold, is used to further obtain a low-dimensional feature vector. Finally, battery capacity degradation is estimated using the geodesic distance on the manifold between the initial and the most recent features. Verification experiments were conducted using data obtained under different operating and aging conditions. Results suggest that the proposed visual cognition approach provides a highly accurate means of estimating battery capacity and thus offers a promising method derived from the emerging field of cognitive computing. |
| format | Article |
| id | doaj-art-5b4e4519593e44cfae40ab79ba9f9023 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5b4e4519593e44cfae40ab79ba9f90232025-02-03T05:44:04ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/63421706342170Lithium-Ion Battery Capacity Estimation: A Method Based on Visual CognitionYujie Cheng0Laifa Tao1Chao Yang2School of Aeronautic Science and Engineering, Beihang University, Beijing, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing, ChinaThis study introduces visual cognition into Lithium-ion battery capacity estimation. The proposed method consists of four steps. First, the acquired charging current or discharge voltage data in each cycle are arranged to form a two-dimensional image. Second, the generated image is decomposed into multiple spatial-frequency channels with a set of orientation subbands by using non-subsampled contourlet transform (NSCT). NSCT imitates the multichannel characteristic of the human visual system (HVS) that provides multiresolution, localization, directionality, and shift invariance. Third, several time-domain indicators of the NSCT coefficients are extracted to form an initial high-dimensional feature vector. Similarly, inspired by the HVS manifold sensing characteristic, the Laplacian eigenmap manifold learning method, which is considered to reveal the evolutionary law of battery performance degradation within a low-dimensional intrinsic manifold, is used to further obtain a low-dimensional feature vector. Finally, battery capacity degradation is estimated using the geodesic distance on the manifold between the initial and the most recent features. Verification experiments were conducted using data obtained under different operating and aging conditions. Results suggest that the proposed visual cognition approach provides a highly accurate means of estimating battery capacity and thus offers a promising method derived from the emerging field of cognitive computing.http://dx.doi.org/10.1155/2017/6342170 |
| spellingShingle | Yujie Cheng Laifa Tao Chao Yang Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition Complexity |
| title | Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition |
| title_full | Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition |
| title_fullStr | Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition |
| title_full_unstemmed | Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition |
| title_short | Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition |
| title_sort | lithium ion battery capacity estimation a method based on visual cognition |
| url | http://dx.doi.org/10.1155/2017/6342170 |
| work_keys_str_mv | AT yujiecheng lithiumionbatterycapacityestimationamethodbasedonvisualcognition AT laifatao lithiumionbatterycapacityestimationamethodbasedonvisualcognition AT chaoyang lithiumionbatterycapacityestimationamethodbasedonvisualcognition |