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Condon A, Buscarino B, Moch E, Sehnert WJ, Miles O, Herring PK, Attia PM. A dataset of over one thousand computed tomography scans of battery cells. Data Brief 2024; 55:110614. [PMID: 39015254 PMCID: PMC11251086 DOI: 10.1016/j.dib.2024.110614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/07/2024] [Accepted: 06/04/2024] [Indexed: 07/18/2024] Open
Abstract
Battery technology is increasingly important for global electrification efforts. However, batteries are highly sensitive to small manufacturing variations that can induce reliability or safety issues. An important technology for battery quality control is computed tomography (CT) scanning, which is widely used for non-destructive 3D inspection across a variety of clinical and industrial applications. Historically, however, the utility of CT scanning for high-volume manufacturing has been limited by its low throughput as well as the difficulty of handling its large file sizes. In this work, we present a dataset of over one thousand CT scans of as-produced commercially available batteries. The dataset spans various chemistries (lithium-ion and sodium-ion) as well as various battery form factors (cylindrical, pouch, and prismatic). We evaluate seven different battery types in total. The manufacturing variability and the presence of battery defects can be observed via this dataset. This dataset may be of interest to scientists and engineers working on battery technology, computer vision, or both.
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Affiliation(s)
- Amariah Condon
- Glimpse, 444 Somerville Avenue, Somerville, MA 02143, United States
| | - Bailey Buscarino
- Glimpse, 444 Somerville Avenue, Somerville, MA 02143, United States
| | - Eric Moch
- Glimpse, 444 Somerville Avenue, Somerville, MA 02143, United States
| | | | - Owen Miles
- Glimpse, 444 Somerville Avenue, Somerville, MA 02143, United States
| | | | - Peter M. Attia
- Glimpse, 444 Somerville Avenue, Somerville, MA 02143, United States
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Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review. ENERGIES 2021. [DOI: 10.3390/en14113276] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Battery degradation is a fundamental concern in battery research, with the biggest challenge being to maintain performance and safety upon usage. From the microstructure of the materials to the design of the cell connectors in modules and their assembly in packs, it is impossible to achieve perfect reproducibility. Small manufacturing or environmental variations will compound big repercussions on pack performance and reliability. This review covers the origins of cell-to-cell variations and inhomogeneities on a multiscale level, their impact on electrochemical performance, as well as their characterization and tracking methods, ranging from the use of large-scale equipment to in operando studies.
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Ran A, Chen S, Zhang S, Liu S, Zhou Z, Nie P, Qian K, Fang L, Zhao SX, Li B, Kang F, Zhou X, Sun H, Zhang X, Wei G. A gradient screening approach for retired lithium-ion batteries based on X-ray computed tomography images. RSC Adv 2020; 10:19117-19123. [PMID: 35518286 PMCID: PMC9053883 DOI: 10.1039/d0ra03602a] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 05/12/2020] [Indexed: 11/21/2022] Open
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.
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Affiliation(s)
- Aihua Ran
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Shuxiao Chen
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Siwei Zhang
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Siyang Liu
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Zihao Zhou
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Pengbo Nie
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Kun Qian
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Lu Fang
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Shi-Xi Zhao
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Baohua Li
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Feiyu Kang
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Xiang Zhou
- School of Data Science, Department of Mathematics, City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong SAR China
| | - Hongbin Sun
- Department of Electrical Engineering, Tsinghua University Beijing China
| | - Xuan Zhang
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
| | - Guodan Wei
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University Shenzhen 518055 China
- Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen 518055 China
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