Abstract
Battery performance is strongly correlated with electrode microstructural properties. Of the relevant properties, the tortuosity factor of the electrolyte transport paths through microstructure pores is important as it limits battery maximum charge/discharge rate, particularly for energy-dense thick electrodes. Tortuosity factor however, is difficult to precisely measure, and thus its estimation has been debated frequently in the literature. Herein, three independent approaches have been applied to quantify the tortuosity factor of lithium-ion battery electrodes. The first approach is a microstructure model based on three-dimensional geometries from X-ray computed tomography (CT) and stochastic reconstructions enhanced with computationally generated carbon/binder domain (CBD), as CT is often unable to resolve the CBD. The second approach uses a macro-homogeneous model to fit electrochemical data at several rates, providing a separate estimation of the tortuosity factor. The third approach experimentally measures tortuosity factor via symmetric cells employing a blocking electrolyte. Comparisons have been made across the three approaches for 14 graphite and nickel-manganese-cobalt oxide electrodes. Analysis suggests that if the tortuosity factor were characterized based on the active material skeleton only, the actual tortuosities would be 1.35–1.81 times higher for calendered electrodes. Correlations are provided for varying porosity, CBD phase interfacial arrangement and solid particle morphology.
Highlights
One such example of effective parameters is the description of ionic transport in the electrolyte phase
A microstructure model has been used to calculate the tortuosity factor of calendered and uncalendered positive NMC and negative graphite electrodes based on a homogenization calculation, performed on a total of 14 microstructural three-dimensional volumes reconstructed from X-ray tomography images and enhanced with numerically generated carbon/binder domain (CBD)
The effect of the CBD on the tortuosity has been evaluated for a wide range of active material microstructures stochastically generated representative of the actual negative and positive electrodes, and for a large range of CBD morphology
Summary
One such example of effective parameters is the description of ionic transport in the electrolyte phase. Many other tortuosity factor-porosity relationships have been derived in the literature.[43,44,45] It is notable that several tortuosity factor-porosity relationships tend to agree in the porosity range of 0.4–0.7, while being significantly different for the lower values (due to different underlying assumptions), questioning the validity range of these expressions.[33] Other microstructure parameters, for instance particle shape and orientation,[26,46,47,48] geometric path length ( called the geometric tortuosity) and variation of the pore section area (factor of constriction)[35,49] have been defined in the literature to provide a better understanding of the tortuosity factor The purpose of these additional parameters is to discriminate the different contributions of the microstructure morphology on the effective diffusion, rather than to encompasses all of them in a unique parameter (the tortuosity factor or the Bruggeman exponent). This explains in part the poor predictive capability of tortuosity factor-porosity relationships for low porosity materials for which the literature reports a wide range of values[26,33,34])
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