![](/images/graphics-bg.png)
Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition
Joint Authors
Yang, Chao
Cheng, Yujie
Tao, Laifa
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-17
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Cheng, Yujie& Tao, Laifa& Yang, Chao. 2017. Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition. Complexity،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1143247
Modern Language Association (MLA)
Cheng, Yujie…[et al.]. Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition. Complexity No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1143247
American Medical Association (AMA)
Cheng, Yujie& Tao, Laifa& Yang, Chao. Lithium-Ion Battery Capacity Estimation: A Method Based on Visual Cognition. Complexity. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1143247
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143247