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The constant current constant voltage charging protocol has been the standard for battery charging because of its simplicity and ease of implementation. However, with the continued integration of batteries into more complex electronics, i.e. electric vehicles, and medical devices, customizing charging schemes based on the particular need has become more appealing. This is especially true when fast charging is necessary. A major challenge in adopting alternative charging protocols is dendritic growth along the anode surface. The dendrites cause safety and efficiency issues. To mitigate the dendritic growth, a variety of specialized charging protocols, i.e. pulse and boost, have been proposed. Through computational modeling, a direct investigation can be conducted to determine which of the parameters for charging must be balanced in order to rapidly charge the battery and suppress dendrite growth. These parameters include the magnitude of the pulsed charge as well as the frequency. Direct observations of dendritic growth can be used to inform which charging protocols will lead to high performance and longer cycle life.

Dendrite formation on the electrode surface of high energy density lithium (Li) batteries causes safety problems and limits their applications. Suppressing dendrite growth could significantly improve Li battery performance. Dendrite growth and morphology is a function of the mixing in the electrolyte near the anode interface. Most research into dendrites in batteries focuses on dendrite formation in isotropic electrolytes (i.e., electrolytes with isotropic diffusion coefficient). In this work, an anisotropic diffusion reaction model is developed to study the anisotropic mixing effect on dendrite growth in Li batteries. The model uses a Lagrangian particle-based method to model dendrite growth in an anisotropic electrolyte solution. The model is verified by comparing the numerical simulation results with analytical solutions, and its accuracy is shown to be better than previous particle-based anisotropic diffusion models. Several parametric studies of dendrite growth in an anisotropic electrolyte are performed and the results demonstrate the effects of anisotropic transport on dendrite growth and morphology, and show the possible advantages of anisotropic electrolytes for dendrite suppression.

Dendrite growth is an ongoing challenge on the anode surface in lithium metal batteries. Over multiple charge and discharge cycles dendritic growth leads to a decrease in battery performance and increased safety issues due to short circuiting. The morphology and growth rate of dendrites depend on a number of factors including the rate and depth of charge/discharge. In this work a computational model of dendrite growth at the interface is used to investigate the effects of cycling on dendrite growth rate and morphology. The model resolves the interfacial chemical-physical processes and simulates multiple charge and discharge cycles. Results show that dendrite growth depends on both the charging and discharging cycles, which can lead to heterogeneous growth patterns based on the levels of deposition and dissolution of lithium during the cycles.

In this paper we investigate transport limitations in the electrodes of lithium-air batteries through computational modeling. We use meso-scale models to consider the effects of dendrites on the current and potential at the anode surface, and to investigate the effects of reaction and transport parameters on the formation of precipitates in the cathode. The formation of dendrites on the anode surface during cycling reduces the transport of ions and can lead to short circuits in the cell. Growth of precipitates in the cathode reduces the specific capacity of the cell due to surface passivation and pore clogging. Both of these degradation mechanisms depend on meso-scale phenomena, such as the pore-scale reactive transport in the cathode. To understand the effects of the meso-scale transport and precipitation on the performance and lifetime of Li-air batteries, meso-scale modeling is needed that is able to resolve the electrodes and their microstructures.

This paper describes a distributed electrochemistry model of the solid oxide fuel cell (SOFC) electrodes and electrolyte. The distributed electrochemistry (DEC) model solves the transport, reactions, and electric potential through the thickness of the SOFC electrodes. The DEC model allows the local conditions within the electrodes to be studied and allows for a better understanding of how electrochemical and microstructural parameters affect the SOFC performance. In this paper the governing equations and implementation of the DEC model are presented along with several case studies which are used to investigate the sensitivity of the SOFC performance to the microstructural and electrochemical parameters of the cathode and to explore methods of improving the electrochemical performance of the SOFC cathode.

High Energy density lithium (Li) batteries are promising advanced energy technologies for applications in the automotive and portable power markets. Advanced Li batteries have several design and operational challenges to overcome before becoming commercially viable. One of these challenges is the formation of dendrites on the anode surface of the Li-air battery over multiple charge and discharge cycles. Dendrite formation decreases cell performance and causes safety concerns due to short circuiting. In this research computational methods are adopted to investigate the physics of dendrite formation in Li-air batteries and to develop strategies to suppress dendrite growth. The effects of convection and anisotropic transport properties in the electrolyte on the morphology and growth rate of dendrites are investigated using a two-dimensional (2D) smoothed particle hydrodynamics (SPH) model. SPH is a mesh-free Lagrangian computational fluid dynamics method, which allows for easy implementation of complex physics at the dendrite surface. Anisotropic diffusion is modeled to study the effects of mixing near the anode-electrolyte interface on dendrite growth. The simulation results suggest that high anisotropy in the electrolyte promotes a robust, compact dendrite structure versus isotropic diffusion cases. Convection effects are also considered to study the electro-osmosis effect on dendrite growth. Circulating flows are included in the model to simulate electro-osmotic flows near the anode-electrolyte interface. The simulation results reveal that the convection effect will increase the mixing near the dendrite and lead to significant changes in dendrite morphology. The results of these computational studies are being used to develop mitigation strategies for dendrite growth in Li-air batteries. These strategies include the use of novel electrolyte solutions and hybrid electrolyte designs, which allow for the tuning of transport properties through the electrolyte. The computational models are being used to investigate these designs and optimize material properties to increase battery performance and lifetime. Figure 1

This research focuses on improving the performance and durability of advanced lithium air (Li-air) battery technologies through computational modeling and materials informatics. In particular, the issue of dendrite growth at the anode-electrolyte interface was investigated through physical models and a materials informatics approach to identify novel electrolyte materials for Li-air batteries. Dendrites form on the anode surface over multiple charge and discharge cycles, causing a decrease in performance and safety concerns due to short circuiting. Dendrite growth depends strongly on the reactive transport near the anode-electrolyte interface and heterogeneities on the anode surface. To reduce dendrite growth, we investigated increasing the mixing near the anode-electrolyte interface through novel electrolyte designs that directionally affect the transport properties of the electrolyte solution. The research was divided into two main tasks: physical modeling of dendrite growth and morphology, and development of a materials informatics approach for identifying novel electrolyte materials. Dendrite growth was investigated using the meso-scale smoothed particle hydrodynamics (SPH) method. SPH is a mesh-free technique that uses a Lagrangian framework to model the simulation domain as a discrete system of particles. SPH particles are used as interpolation points to discretize and solve the governing partial differential equations of a system based on the SPH smoothing function. Due to its Lagrangian nature, SPH does not require explicit boundary tracking, which allows for simple implementation of complex geometries and moving boundaries, such as the dendrite formation at the anode-electrolyte interface. A materials informatics approach was used in a discovery mode to identify promising material combinations for the electrolyte. Our research focused on developing the materials informatics framework for electrolyte solutions. The developed framework is general and is applicable to other materials systems for batteries and for other application areas. The informatics techniques have predictive capabilities based upon an electrolyte materials knowledge base, allowing us to evaluate materials based on their thermodynamic properties and the desired properties needed for a specific application.

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