Multi-Timescale Modeling for Optimizing Battery Management Systems in Electric Vehicles

Description

Winter haze is a major atmospheric pollution issue all over the world, as it can cause serious transport disruption (due to low visibility) and major health problems. A severe haze event is generally accompanied by an abrupt rise in the mass concentration of fine particles and an increase in mean particle size. At the same time, the number concentration of particles remains stable, or even declines, which suggests an intensive agglomeration of aerosol particles. Understanding the evolution of particle mass concentration (PMC), particle number concentration (PNC), and particle size distribution (PSD) through long-term monitoring and subsequent pattern induction is fundamental to understanding haze formation. However, the physical-mechanical behaviors of particles are unstable due to the complicated effects of Brownian motion of molecules, gravity settling of particles, atmospheric turbulence, and multiple micro-adhesive interactions, which influence the formation of aerosol particles. Such micro-effects are also influenced by meteorological conditions, especially humidity; these conditions cause macro-phenomena among aerosol particles, such as collision, agglomeration, and breakage. Because of these complications, it is critical to develop numerical simulations to depict particle behaviors. These will permit further investigation of the dynamic, multi-scale evolution of particle transport and contribute to developing efficient remediation policies that minimize severe haze events’ regional impacts.The proposed project will develop new statistical variables for analyzing time-series monitored data on air pollution, and adopt a population balance equation (PBE) to distinguish physical and chemical effects on particle behaviors in selected, typical mega-cities. It will clarify the role of meteorological factors during the early stages of severe haze event formation (such as the abrupt rise of PMC) and identify meteorological factors in the formation of severe haze. The project will include both experimental measurements and numerical analyses. The statistical data (field monitoring) and charge characteristics (electric experiment) of aerosol particles will set the initial conditions for computational modeling. For the mechanical model of particle evolution, a discrete element model (DEM) will be constructed to cover microadhesive interactions among fine particles (i.e., van der Waals force, liquid bridge force, and electrostatic force) and Brownian motion in the atmosphere system. The population balance model (PBM), which considers particle agglomeration, breakage, nucleation, and growth, will be coupled with the Eulerian-Lagrangian method using gas-solid, two-phase flow to simulate particle dynamics, to assess the impact of meteorological factors on severe haze formation.