The widespread use of energy storage technologies in the power grid, particularly in the form of batteries will have a significant impact. The current grid is extremely inefficient, as it operates almost at full capacity during the day, but it is significantly underutilized during the evening and nighttime hours. The ability to store the energy generated at night and release it into the grid during the day, will significantly ease the power demand on the current grid, which otherwise would require the building of new power plants (accompanied by an increase in emissions). In addition to the efficient use of our current infrastructure, energy storage is key to the integration of renewable forms of energy, particularly solar and wind into our power grid, because these technologies cannot generate power on-demand. Our collaboration here is to help to further develop the unique technology that EOS has developed as an energy storage solution, Zn-Hybrid Cathode Batteries.
Eos’ technology offers significant advantages over current technologies. Their proprietary Zinc hybrid cathode chemistry employs abundant, low-cost materials and manufacturing methods. An aqueous electrolyte enables system safety.
The system supports 100% depth of discharge and an operating temperature range of 10-45°C mitigates the need for dedicated heating/cooling. Even with all these advantages, the aim is to constantly improve the battery technology so that the increasing needs of grid storage, especially as the proportion of renewable energy sources start to increase.
At Stony Brook, we have developed computational models to study the processes that occur in batteries, and have studied the onset of the dendritic transition. Our models are easily adapted to the Eos battery system, and will allow us to examine processes and mechanisms in these batteries, with a goal to creating operating maps and strategies to improve the battery.
This has all been made possible through funding from the Division of Science Technology and Innovation (NYSTAR) of the Empire State Development Corporation (ESD) through its High Performance Computing Consortium (hpc-ny.org) and the New York State Regional Economic Development Council.
The study focused on developing a Lattice Boltzmann model (LBM) to study the formation of dendrites in the Zn-Hybrid cathode system. LBM was introduced three decades ago, and now it has been developed into a powerful tool for Computational Fluid Dynamics (CFD). This method is particularly successful in fluid flow applications involving interfacial dynamics and complex boundaries. This implementation of the LBM method (at Stony Brook) for the full cycle of charge and discharge is a significant development as it allows us to follow the formation of microstructure on the anode of the battery, and thus, design strategies that can increase the capacity of the battery without allowing dendrites (a primary failure mechanism) to form on the surface of the anode.
The first step was to develop a list of parameters that could be used in our model.
These were developed by looking at literature data, as well as consulting with EOS engineers on parameters that would work for their system. We then proceeded to calibrate the model by comparing the time to dendrite formation. We then ran a series of sensitivity studies to examine the role of different factors on dendrite formation (for example the dielectric constant, and the concentration of the electrolyte). These studies allowed us to examine which of these system parameters could be changed in order to have the largest effect on the performance of the battery. We were also able to generate phase diagrams (operating maps) of when dendrites would form in this system. We were also able to visualize how dendrites formed in the system as the charging rate is increased (see Figure 1 below).