• Energy Research

Silicon/graphite blended electrodes are promising candidates to replace graphite in lithium ion batteries, benefiting from the high capacity of silicon and the good structural stability of carbon. Models have proven essential to understand and optimise batteries with new materials. However, most previous models treat silicon/graphite blends as a single “lumped” material, offering limited understanding of the behaviors of the individual materials and thus limited design capability. Here, we present a multi-material model for silicon/graphite electrodes with detailed descriptions of the contributions of the individual active materials. The model shows that silicon introduces voltage hysteresis to silicon/graphite electrodes and consequently a “plateau shift” during delithiation of the electrodes. There will also be competition between the silicon and graphite lithiation reactions depending on silicon/graphite ratio. A dimensionless competing factor is derived to quantify the competition between the two active materials. This is demonstrated to be a useful indicator for active operating regions for each material and we demonstrate how it can be used to design cycling protocols for mitigating electrode degradation. The multi-material electrode model can be readily implemented into full-cell models and coupled with other physics to guide further development of lithium ion batteries with silicon-based electrodes.

Solid oxide fuel cells are high-efficiency renewable energy devices and considered one of the most promising net-zero carbon energy technologies. Numerical modelling is a powerful tool for the virtual design and optimisation of the next-generation solid oxide fuel cells but needs to tackle issues for incorporating the multi-scale character of the cell and further improving the accuracy and computational efficiency. While most of solid oxide fuel cell models were developed based on closed source platforms which limit the freedom of customisation in numerical discretization schemes and community participation. Here, an open source multi-physics and multiscale platform for advanced SOFC simulations consisting of both cell- and pore-scale performance models was developed using OpenFOAM. The modelling aspects are elucidated in detail, involving the coupling of various physical equations and the implementation of the pore-scale electrode in the performance model. The entire platform was carefully validated against experimental data and the other numerical models which were implemented in commercial software ANSYS Fluent and based on the lattice Boltzmann method. The cell-scale model is subsequently employed to study the effects of different fuels, i.e., pure hydrogen and different ratios of pre-reformed methane gas under various operating temperatures. It is found that the cell-scale model reasonably predicts the effects of these parameters on the cell performance, aligning well with the Fluent model. This study further identified the size of representative element volume with respect to the current density for the anode via the pore-scale model where the realistic microstructures reconstructed by a Xe plasma focused ion beam–scanning electron microscopy are employed as computational domains. It is found that a volume element size of 1243 voxels is sufficient to yield the representative current density of the whole. All these numerical investigations show that OpenFOAM is a potential multi-physics and multi-scale computational platform that is capable of accurately predicting both cell-scale and pore-scale performance and spatial information of solid oxide fuel cells. The developed models are also made public in GitHub to inspire community to further develop around it.

Abstract Proton exchange membrane (PEM) fuel cells are a promising candidate as the next-generation power sources for portable, transportation, and stationary applications. Gas diffusion layers (GDL) coated with microporous layers (MPL) are a vital component of PEM fuel cells, providing multiple functions of mechanical support, reactant transport, liquid water removal, waste heat removal, and electron conductance. In this review, we explain several most important aspects in the research and development (R&D) of this fuel cell component, including material characterization, liquid water detection/quantitation, structure reconstruction, fundamental modeling, transport properties, and durability. Specially, the commonly used microstructure reconstruction methods for GDLs are presented and discussed. Visualization techniques for liquid water detection in the GDL and MPL microstructures are described. Major modeling approaches, such as the multiphase mixture (M2) formulation, pore networks model (PNM), lattice Boltzmann method (LBM) and volume of fluid (VOF) approach, are reviewed and explained. Important material properties and parameters that greatly influence two-phase flow and fuel cell performance, and GDL-related material degradation issues are discussed and summarized to further advance on the GDL material design and development.

Abstract For high-performance low-temperature fuel cells (e.g. hydrogen proton exchange membrane fuel cell for powering vehicles), significant amount of reactant needs to be supplied, leading to turbulent two-phase flow, which is largely ignored in previous studies. In this study, a direct numerical simulation (DNS) model of the two-phase turbulent flow in fuel cell flow channel is developed with a modified volume-of-fluid (VOF) approach for tracking the air/water interface. The turbulent flow inlet of the two-phase DNS model is obtained from a validated single-phase DNS model. By resolving the whole range of spatial and temporal scales of turbulence, the results of the two-phase DNS model show that the deformation of water droplet is asymmetric and broken into small pieces/films, and is significantly different from the laminar and the corresponding k − e models. It is suggested that the turbulence effect on the two-phase transport in fuel cell flow channel is significant and needs to be considered for water management by using the DNS model.

Abstract Multidimensional numerical models are useful tools for understanding the heat transfer mechanisms and performance optimization of thermoelectric generators (TEGs). In this study, two three-dimensional numerical models are developed for TEGs based on different formulations, but with similar abilities for heat and electricity transfer analysis and performance prediction. Model 1 solves the conservation equations of the Seebeck potential and the Ohmic potential separately, and the total built-in potential can be obtained based on the solved Seebeck and Ohmic potentials. Model 2 solves the conservation equation of the total built-in potential directly, and the conservation equation for the Ohmic potential is also solved. The comparison between Model 1 and Model 2 shows that Model 2 is slightly more precise for power output prediction. The detailed formulations of these two models are described, and the difference among the present and previous models is also discussed. Some important modeling aspects are elucidated for the TEG models, such as the conservation equations and boundary conditions. Parametric studies are carried out based on various thermal boundary conditions. The influence of the TEG semiconductor shape on performance is investigated in details. It is found that for the nearly same volume of semiconductor materials, changing the shape from normal cuboid (constant cross-sectional area) to hexahedrons (variable cross-sectional area) could increase the power output significantly. The reason is that the temperature gradient could be enhanced when proper variable cross-sectional areas are used.

Abstract We derive a power factor and an efficiency factor for comprehensive evaluation of thermoelectric generator (TEG) materials, and approve that only using the power factor (or efficiency factor) is sufficient to determine the output power (or thermoelectric conversion efficiency) potential of a material. We also show that the effect of thermal conductivity is underestimated in the traditionally used figure of merit. In fact, the traditionally used power factor and figure of merit are simplified versions of the new factors for the special “constant surface temperatures” condition (the heat transfer coefficients on the hot and cold sides are infinitely large), which rarely occurs in practical applications. These two new factors can be conveniently used for material evaluation and design optimization, for example, to determine the optimal interface temperature and length ratio of a segmented TEG.

The digital transformation empowered by artificial intelligence will create huge opportunities for the porous energy materials research community.