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 are closely correlated with their internal resistance and capacity, indicating the feasibility of CT scores to sort retired batteries. We find out that when the CT scores are larger than 0.65, there is high potential for a secondary application. Therefore, this pioneering and non-destructive CT score method can reflect the internal electrochemical properties of these retired batteries, which could potentially expedite the battery reuse industry for a sustainable energy future.