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A numerical method for calculating the mass transfer coefficient in fibrous media is presented. First, pressure driven flow was modelled using the Lattice Boltzmann Method. The advection-diffusion equation was solved for convective-reacting porous media flow, and the method is contrasted with experimental methods such as the limiting current diffusion technique, for its ability to determine and simulate mass transfer systems that are operating at low Reynolds number flows. A series of simulations were performed on three materials; specifically, commercially available carbon felts, electrospun carbon fibers and electrospun carbon fibers with anisotropy introduced to the microstructure. Simulations were performed in each principal direction (x,y,z) for each material in order to determine the effects of anisotropy on the mass transfer coefficient. In addition, the simulations spanned multiple Reynolds and Péclet numbers, to fully represent highly advective and highly diffusive systems. The resulting mass transfer coefficients were compared with values predicted by common correlations and a good agreement was found at high Reynolds numbers, but less so at lower Reynolds number typical of cell operation, reinforcing the utility of the numerical approach. Dimensionless mass transfer correlations were determined for each material and each direction in terms of the Sherwood number. These correlations were analyzed with respect to each materials' permeability tensor. It was found that as the permeability of the system increases, the expected mass transfer coefficient decreases. Two general mass transfer correlations are presented, one correlation for isotropic fibrous media and the other for through-plane flow in planar fibrous materials such as electrospun media and carbon paper. The correlations are Sh = 0.879 Re 0.402 Sc 0.390 and Sh = 0.906 Re 0.432 Sc 0.432 respectively.

The electrode drying process is a crucial step in the manufacturing of lithium-ion batteries and can significantly affect the performance of an electrode once stacked in a cell. High drying rates may induce binder migration, which is largely governed by the temperature. Additionally, elevated drying rates will result in a heterogeneous distribution of the soluble and dispersed binder throughout the electrode, potentially accumulating at the surface. The optimized drying rate during the electrode manufacturing process will promote balanced homogeneous binder distribution throughout the electrode film; however, there is a need to develop more informative in situ metrologies to better understand the dynamics of the drying process. Here, ultrasound acoustic-based techniques were developed as an in situ tool to study the electrode drying process using NMC622-based cathodes and graphite-based anodes. The drying dynamic evolution for cathodes dried at 40 and 60 °C and anodes dried at 60 °C were investigated, with the attenuation of the reflective acoustic signals used to indicate the evolution of the physical properties of the electrode-coating film. The drying-induced acoustic signal shifts were discussed critically and correlated to the reported three-stage drying mechanism, offering a new mode for investigating the dynamic drying process. Ultrasound acoustic-based measurements have been successfully shown to be a novel in situ metrology to acquire dynamic drying profiles of lithium-ion battery electrodes. The findings would potentially fulfil the research gaps between acquiring dynamic data continuously for a drying mechanism study and the existing research metrology, as most of the published drying mechanism research studies are based on simulated drying processes. It shows great potential for further development and understanding of the drying process to achieve a more controllable electrode manufacturing process.

Lithium-sulfur (Li-S) batteries are promising next-generation rechargeable energy storage systems due to their high energy density and use of abundant and inexpensive materials. However, rapid self-discharge and poor cycle stability due to the solubility of intermediate polysulfide conversion products have slowed their commercialization. Herein, we provide a detailed account of the multiphasic reactions occurring during self-discharge of a Li-S battery held at various degrees of discharge (DOD) through both simulation and experiment. For the first time, self-discharge of a full Li-S battery is simulated using a 1D model to describe reactions at both the anode and cathode. The model accurately describes experimentally derived results obtained over the longest durations of self-discharge studied to date (140 h). This validated model was used to follow the reversible and irreversible capacity loss caused by shuttling and precipitation of insoluble Li2S2 and Li2S as a function of DOD. While the most rapid self-discharge is observed at low DOD, this also leads to the smallest irreversible loss. The results suggest that resting a Li-S battery near 2.1 V minimizes both reversible and irreversible losses.

Over the last decade, acoustic methods, including acoustic emission (AE) and ultrasonic testing (UT), have been increasingly deployed for process diagnostics and health monitoring of electrochemical power devices, including batteries, fuel cells, and water electrolysers. These techniques are non-invasive, highly sensitive, and low-cost, providing a high level of spatial and temporal resolution and practicality. Their application in electrochemical devices is based on identifying changes in acoustic signals emitted from or propagated through materials as a result of physical, structural, and electrochemical changes within the material. These changes in acoustic signals are then correlated to critical processes and the health status of these devices. This review summarises progress in the use of acoustic methods for the process and health monitoring of major electrochemical energy conversion and storage devices. First, the fundamental principles of AE and UT are introduced, and then the application of these acoustic techniques to electrochemical power devices are discussed. Conclusions and perspectives on some of the key challenges and potential commercial and academic applications of the devices are highlighted. It is expected that, with further developments, acoustic techniques will form a key part of the suite of diagnostic techniques routinely used to monitor electrochemical devices across various processes, including fabrication, post-mortem examination and recycle decision support to aid the deployment of these devices in increasingly demanding applications.

Physics-based electrochemical battery models derived from porous electrode theory are a very powerful tool for understanding lithium-ion batteries, as well as for improving their design and management. Different model fidelity, and thus model complexity, is needed for different applications. For example, in battery design we can afford longer computational times and the use of powerful computers, while for real-time battery control (e.g. in electric vehicles) we need to perform very fast calculations using simple devices. For this reason, simplified models that retain most of the features at a lower computational cost are widely used. Even though in the literature we often find these simplified models posed independently, leading to inconsistencies between models, they can actually be derived from more complicated models using a unified and systematic framework. In this review, we showcase this reductive framework, starting from a high-fidelity microscale model and reducing it all the way down to the Single Particle Model (SPM), deriving in the process other common models, such as the Doyle-Fuller-Newman (DFN) model. We also provide a critical discussion on the advantages and shortcomings of each of the models, which can aid model selection for a particular application. Finally, we provide an overview of possible extensions to the models, with a special focus on thermal models. Any of these extensions could be incorporated into the microscale model and the reductive framework re-applied to lead to a new generation of simplified, multi-physics models.

Dual network extraction algorithm to investigate multiple transport processes in porous materials: Image-based modeling of pore and grain scale processes, Computers and Chemical Engineering (2018), doi: