Li-ion battery performance is strongly influenced by the 3D microstructure of its cathode particles.Cracks within these particles develop during calendaring and cycling,reducing connectivity but increasing reactive su...Li-ion battery performance is strongly influenced by the 3D microstructure of its cathode particles.Cracks within these particles develop during calendaring and cycling,reducing connectivity but increasing reactive surface,making their impact on battery performance complex.Understanding these contradictory effects requires a quantitative link between particle morphology and battery performance.However,informative 3D imaging techniques are time-consuming,costly and rarely available,such that analyses often have to rely on 2D image data.This paper presents a novel stereological approach for generating virtual 3D cathode particles exhibiting crack networks that are statistically equivalent to those observed in 2D sections of experimentally measured particles.Consequently,2D image data suffices for deriving a full 3D characterization of cracked cathodes particles.Such virtually generated 3D particles could serve as geometry input for spatially resolved electro-chemo-mechanical simulations to enhance our understanding of structure-property relationships of cathodes in Li-ion batteries.展开更多
Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling.Information is dra...Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling.Information is drawn from multimodal microscopy techniques to artificially generate LiNi_(0.5)Mn_(0.3)Co_(0.2)O_(2) particles with full sub-particle grain detail.Statistical representations of particle architectures are derived from X-ray nano-computed tomography data supporting an‘outer shell’model,and sub-particle grain representations are derived from focused-ion beam electron backscatter diffraction data supporting a‘grain’model.A random field model used to characterize and generate the outer shells,and a random tessellation model used to characterize and generate grain architectures,are combined to form a multi-scale model for the generation of virtual electrode particles with fullgrain detail.This work demonstrates the possibility of generating representative single electrode particle architectures for modeling and characterization that can guide synthesis approaches of particle architectures with enhanced performance.展开更多
For a deeper understanding of the functional behavior of energy materials,it is necessary to investigate their microstructure,e.g.,via imaging techniques like scanning electron microscopy (SEM).However,active material...For a deeper understanding of the functional behavior of energy materials,it is necessary to investigate their microstructure,e.g.,via imaging techniques like scanning electron microscopy (SEM).However,active materials are often heterogeneous,necessitating quantification of features over large volumes to achieve representativity which often requires reduced resolution for large fields of view.Cracks within Li-ion electrode particles are an example of fine features,representative quantification of which requires large volumes of tens of particles.To overcome the trade-off between the imaged volume of the material and the resolution achieved,we deploy generative adversarial networks (GAN),namely SRGANs,to super-resolve SEM images of cracked cathode materials.A quantitative analysis indicates that SRGANs outperform various other networks for crack detection within aged cathode particles.This makes GANs viable for performing super-resolution on microscopy images for mitigating the trade-off between resolution and field of view,thus enabling representative quantification of fine features.展开更多
基金Funding was provided by DOE’s Vehicle Technologies Office,Extreme Fast Charge and Cell Evaluation of Lithium-ion Batteries Program,Jake Herb,Technology Manager.The views expressed in the article do not necessarily represent the views of the DOE or the U.S.Government.
文摘Li-ion battery performance is strongly influenced by the 3D microstructure of its cathode particles.Cracks within these particles develop during calendaring and cycling,reducing connectivity but increasing reactive surface,making their impact on battery performance complex.Understanding these contradictory effects requires a quantitative link between particle morphology and battery performance.However,informative 3D imaging techniques are time-consuming,costly and rarely available,such that analyses often have to rely on 2D image data.This paper presents a novel stereological approach for generating virtual 3D cathode particles exhibiting crack networks that are statistically equivalent to those observed in 2D sections of experimentally measured particles.Consequently,2D image data suffices for deriving a full 3D characterization of cracked cathodes particles.Such virtually generated 3D particles could serve as geometry input for spatially resolved electro-chemo-mechanical simulations to enhance our understanding of structure-property relationships of cathodes in Li-ion batteries.
基金This work was authored in part by the National Renewable Energy Laboratory,operated by Alliance for Sustainable Energy,LLC,for the U.S.Department of Energy(DOE)under Contract No.DE-AC36-08GO28308Funding was provided by the U.S.DOE Office of Vehicle Technology Extreme Fast Charge Program,program manager Samuel Gillard.The views expressed in the article do not necessarily represent the views of the DOE or the U.S.Government.The U.S.Government retains and the publisher,by accepting the article for publication,acknowledges that the U.S.Government retains a nonexclusive,paid-up,irrevocable,worldwide license to publish or reproduce the published form of this work,or allow others to do so,for U.S.Government purposes.
文摘Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling.Information is drawn from multimodal microscopy techniques to artificially generate LiNi_(0.5)Mn_(0.3)Co_(0.2)O_(2) particles with full sub-particle grain detail.Statistical representations of particle architectures are derived from X-ray nano-computed tomography data supporting an‘outer shell’model,and sub-particle grain representations are derived from focused-ion beam electron backscatter diffraction data supporting a‘grain’model.A random field model used to characterize and generate the outer shells,and a random tessellation model used to characterize and generate grain architectures,are combined to form a multi-scale model for the generation of virtual electrode particles with fullgrain detail.This work demonstrates the possibility of generating representative single electrode particle architectures for modeling and characterization that can guide synthesis approaches of particle architectures with enhanced performance.
文摘For a deeper understanding of the functional behavior of energy materials,it is necessary to investigate their microstructure,e.g.,via imaging techniques like scanning electron microscopy (SEM).However,active materials are often heterogeneous,necessitating quantification of features over large volumes to achieve representativity which often requires reduced resolution for large fields of view.Cracks within Li-ion electrode particles are an example of fine features,representative quantification of which requires large volumes of tens of particles.To overcome the trade-off between the imaged volume of the material and the resolution achieved,we deploy generative adversarial networks (GAN),namely SRGANs,to super-resolve SEM images of cracked cathode materials.A quantitative analysis indicates that SRGANs outperform various other networks for crack detection within aged cathode particles.This makes GANs viable for performing super-resolution on microscopy images for mitigating the trade-off between resolution and field of view,thus enabling representative quantification of fine features.
