A gradient screening approach for retired lithium-ion batteries based on X-ray computed tomography images. Issue 32 (20th May 2020)
- Record Type:
- Journal Article
- Title:
- A gradient screening approach for retired lithium-ion batteries based on X-ray computed tomography images. Issue 32 (20th May 2020)
- Main Title:
- A gradient screening approach for retired lithium-ion batteries based on X-ray computed tomography images
- Authors:
- Ran, Aihua
Chen, Shuxiao
Zhang, Siwei
Liu, Siyang
Zhou, Zihao
Nie, Pengbo
Qian, Kun
Fang, Lu
Zhao, Shi-Xi
Li, Baohua
Kang, Feiyu
Zhou, Xiang
Sun, Hongbin
Zhang, Xuan
Wei, Guodan - Abstract:
- 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. 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 sustainableAbstract : 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. 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. … (more)
- Is Part Of:
- RSC advances. Volume 10:Issue 32(2020)
- Journal:
- RSC advances
- Issue:
- Volume 10:Issue 32(2020)
- Issue Display:
- Volume 10, Issue 32 (2020)
- Year:
- 2020
- Volume:
- 10
- Issue:
- 32
- Issue Sort Value:
- 2020-0010-0032-0000
- Page Start:
- 19117
- Page End:
- 19123
- Publication Date:
- 2020-05-20
- Subjects:
- Chemistry -- Periodicals
540.5 - Journal URLs:
- http://pubs.rsc.org/en/Journals/JournalIssues/RA ↗
http://www.rsc.org/ ↗ - DOI:
- 10.1039/d0ra03602a ↗
- Languages:
- English
- ISSNs:
- 2046-2069
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8036.750300
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 13835.xml