A gradient screening approach for retired lithium-ion batteries based on X-ray computed tomography images.

ABSTRACT: Accurate and efficient screening of retired lithium-ion batteries from electric vehicles is crucial to guarantee reliable secondary applications such as in energy storage, electric bicycles, and smart grids. However, conventional electrochemical screening methods typically involve a charge/discharge process and usually take hours to measure critical parameters such as capacity, resistance, and voltage. To address this issue of low efficiency for battery screening, scanned X-ray Computed Tomography (CT) cross-sectional images in combination with a computational image recognition algorithm have been employed to explore the gradient screening of these retired batteries. Based on the Structural Similarity Index Measure (SSIM) algorithm with 2000 CT images per battery, the calculated CT scores

SUBMITTER: Ran A 

PROVIDER: S-EPMC9053883 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature