Abstract
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.
Highlights
Lithium-ion (Li-ion) batteries, featuring high energy density and light in weight, are becoming more and more popular for various applications, especially in the field of aerospace and electric vehicles [1,2,3]
As we look at an object with conditions such as scale and illumination changing, the signals carried from the eyes to the brain by the millions of axons in the optic nerve are constantly in flux
This study proposes a novel method for estimating the capacity of Li-ion batteries based on visual cognition
Summary
Lithium-ion (Li-ion) batteries, featuring high energy density and light in weight, are becoming more and more popular for various applications, especially in the field of aerospace and electric vehicles [1,2,3]. Most of the existing approaches are model-based methods, including electrochemical [5], equivalent circuit-based [6], and analytical [7, 8] models These models are mostly based on complex physical and chemical processes that take into account the dynamic behavior of batteries [9,10,11], and the estimation performance is highly dependent on the accuracy of the models. These types of models are usually difficult to establish given the restrictions on acquisition of knowledge of the electrochemical parameters, aging mechanisms, and properties of batteries [12]. These original data stored as discrete values are employed to create a lookup table database on the charge status of the master
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