Abstract
Although “post” Li-ion battery is arising as an inevitable solution for sustainable energy transition, it would be unwise to assume ‘conventional’ Li-ion battery is approaching the end of their era; as many strategies are still available to improve their performance. While progress has been continuously achieved to get even better active materials, industry engineers and academic researchers have kept improving on the electrode scale. The most direct way can be done through the microstructure design. In light of this, one attempts to understand the interplay between the electrode microstructure and its performance in this work, which plays a vital role in achieving high-performance Li-ion battery electrodes. This work relies on three major pillars, which are electrochemical measurements, tomography and numerical modeling. The LiNi0.5Mn0.3Co0.2O2 industry-grade electrodes are investigated, since LiNixMnyCo(1-x-y)O2 materials constitute a popular class of cathode materials. The first part of this work focuses on the first two pillars that allow a complete characterization of porous electrodes, comprising both electrochemical performance and microstructural properties. Appropriate experimental methods are carried out to determine the transport properties of the electrodes, such as electrode tortuosity factor and effective electronic conductivity. Thin electrodes are made for the determination of active-material intrinsic properties. The performance of industry-grade electrodes is then assessed through discharge rate capability. A complete quantitative analysis of the microstructures of industry-grade electrodes using the X-ray holotomography technique is performed. The microstructural heterogeneities are quantified for each phase (active materials, carbon binder domain, pore space) separately, along with the statistical quantification of their inter-connectivity at the particle scale. Besides, Operando X-ray Absorption Near Edge Structure coupled with transmission X-ray nano computed tomography are done, offering a direct correlation between electrode microstructure and local electrochemical performance. Also, an image quality assessment method is investigated, which utilizes convolutional neural networks. It can be a direct tool to produce reliable segmentation results and guide the image pre-processing step (e.g. denoise, contrast enhancement) for quality enhancement.The second part relies on the numerical approach to further understand the underlying physics of the electrode during operation. One starts with introducing a new concept of the tortuosity factor, which is demonstrated through a numerical approach to be more appropriate for porous electrodes. A model representing the symmetric cell method is implemented in an open-source application called TauFactor for the electrode tortuosity factor determination using tomographic data. Then, the performance of four industry-grade electrodes is investigated through mathematical models. The parametrization of the models is carried out carefully using appropriate experimental methods. As the Newman pseudo-2D model fails to capture the behavior of the set of electrodes, the formation of porous agglomerates due to the calendering process to achieve high-energy-density is identified to be responsible for this discrepancy. Thus, porous agglomerates are included in the Newman pseudo-2D model. The validation of the electrodes with different electrolytes is done. As a result, the porous agglomerate effects are identified as a dominant limiting factor at high C-rates for high-energy-density electrodes