The Impact of 3D Microscopy Strategies on Computational Analysis for Battery Research

Rapidly progressing global warming and pricing of natural fuels are well-known reasons to search for renewable energy sources. Many nations are addressing global climate change by implementing legislation that favors electric vehicles (EV) and related infrastructure development. The idea of a greener vehicle with no harmful CO2 emission is attractive for most drivers. However, multi-hour charging times and limited distance range are the downsides that keep many car shoppers away from EVs. <br/>Striving for the electrification of transportation sets up new, challenging EV battery standards. Fast charging ability (“fueling time”) and battery capacity (vehicle’s distance range) are the key metrics for Lithium-Ion Battery (LIB) performance. LIBs with graphite anode suffer from rapid capacity fade and Li plating at high charging rates. Degraded battery capacity limits the range of applications, and Li plating becomes a safety concern once conductive paths between anode and cathode are formed. Overpotentials in intercalation electrodes cause both of these effects during fast charging. Li ions diffusion limitation at high charging current is one of the problems that results in electrode polarization that causes overpotentials. There are two pathways to improve Li ion diffusion: to increase the rate of interfacial reactions and to reduce the tortuosity of Li ions paths. The first approach involves altering electrolyte composition and SEI properties. The second approach is concerned with the electrode architecture. The electrode microstructure is the result of a multi-step manufacturing process that is concerned with uniform distribution of the electrode components (active particles, binder and conductive additive) and constant thickness. These microstructures call for characterization methods that cover several orders of magnitude in the length scale from a nm up through micrometers and beyond. Particularly important parameters for fast charging performance are particle connectivity and size, electrode tortuosity, and surface area and, due to the nature of the parameters, only 3D techniques enable adequate characterization. <br/>3D FIB-SEM tomography presents a powerful approach to 3D microstructural characterization of battery electrodes and, when combined with computational modelling packages, enables robust analysis, modelling, and design workflows for fast-charging applications. However, because FIB-SEM tomography operates on the slice-and-view principle, care must be taken in acquiring and analyzing the data to ensure accurate results. <br/>In this study we explore how the 3D FIB-SEM tomography parameters such as slice thickness, slice thickness variation, slice-to-slice alignment, and signal-to-noise level influence resulting electrode microstructure characterization parameters. We control slice thickness, regularity, and alignment by using advanced FIB-SEM acquisition software like Atlas 3D that allows for nm precision. Then, tomography data is plugged into simulation software for the quantitative analysis and battery performance prediction by comparing the cell overpotential to the open-circuit voltage at different charge rates. This approach links electrode manufacturing parameters directly to the estimation of available capacity at various charging rates. Computational simulation of the battery performance based on realistic electrode properties provided by 3D FIB-SEM tomography reduces the need for battery assembly, testing and post-mortem analysis and streamlines the electrode architecture design for fast-charging applications. This work investigates the interplay between data collection approaches and computational output within this framework and provides guidance for ultimate success.