Computational study of understanding and reducing dendrite growth in high energy density batteries

Abstract

Dendrite formation on the electrode surface of high energy density batteries, such as lithium (Li) batteries, causes safety problems and limits their applications. Suppressing dendrite growth could significantly improve Li battery performance. In this thesis, computational models are developed to investigate the physics of dendrite formation in Li batteries after nucleation, which strongly depends on the local mass transport. Dendrite growth in various scenarios is studied, such as in an anisotropic electrolyte, a convective electrolyte and structured electrolytes, to understand the effects of mass transport on growth and to investigate mitigation strategies. Various electrolytes lead to different effects on the local mass transport and eventually affect the dendrite morphology in each scenario. Two numerical methods are used in this thesis. The finite difference method (FDM) is adopted to quickly solve the 1D transient mass transport governing equation and the electrostatic Poisson equation. For the more complex 2D reactive mass transport model, the smoothed particle hydrodynamics (SPH) method is employed. The intrinsic advantages of SPH, such as its mesh-free Lagrangian nature, easy implementation of complex physics at the dendrite surface and the well-developed flow modeling capabilities, make it particularly well suited for modeling dendrite growth in the various scenarios studied in this thesis. Based on the results of these computational studies suggestions for improved battery performance are discussed including material properties, such as diffusivity and viscosity of the electrolyte, and cell design improvements such as porosity and tortuosity of a structured electrolyte. These computational studies can help to reduce dendrite growth by suggesting novel battery designs, and play an important part in the development of more stable and reliable high energy density Li batteries.