Zinc-anode batteries have been studied as a low-cost, long cycle life system for grid-scale energy storage. Kannan Premnath, assistant professor of mechanical engineering, is involved in the development and application of efficient methods for the simulation of electrodeposition phenomena in these batteries to help improve their design and operating conditions.
“Energy storage represents a critical component in the redistribution and use of both conventional and renewable sources of energy for different applications,” he says. “It is important to develop new technologies that are reliable and cost-effective with minimal negative influence on the environment.”
In zinc-anode batteries, the electrodeposition and dissolution of an electrode occurs during charging and discharging cycles, respectively. The electrodeposition often results in the formation of pointed needle-like structures on the electrodes known as dendrites, which are undesirable because their growth over time impedes the performance of batteries.
“The mechanism of electrodeposition is complicated because the morphology of the deposits depends strongly on the operating parameters like the applied voltage and electrolyte concentration,” says Premnath. “Thus, it is crucially important to understand the complex underlying processes involved in the electrodeposition phenomena using modeling and simulations. We have developed algorithms to make computations for these simulations more efficient.”
This research, which is in collaboration with the City University of New York Energy Institute, involves state-of-the-art modeling and simulation methods, including phase field models, lattice Boltzmann methods, multigrid schemes and their implementation on large parallel computer clusters. These physics-based simulations provide a fundamental understanding of the various multiscale flow and interfacial processes in these batteries, which offer valuable insights for their design and improvements. Such simulations are computationally very intensive and involve the use of advanced multiphysics models. By developing more efficient methods, Premnath and his fellow researchers will enable faster simulations of large-scale problems under different parametric conditions.
“The new method resulted in several orders of improvements in computational efficiency while delivering accurate physical results that matched well with recent measurement data thereby demonstrating its predictive capabilities,” he says.