Amassive market penetration of electric vehicles(EVs)associated with nonnegligible energy consumption and environmental issues has imposed a big challenge on evaluating electrical power distribution and related transp...Amassive market penetration of electric vehicles(EVs)associated with nonnegligible energy consumption and environmental issues has imposed a big challenge on evaluating electrical power distribution and related transportation facilities improvement in response to the largescale EV charging service need.Strategical deployment of EV charging stations including location and determination ofnumber of slowcharging stations and fast charging stationshas become an emerging concern and one of the most pressing needs in planning.This paper conducts a comprehensive survey of EV charging demand and distribution models with consideration of realistic driver behaviors impacts.This is currently a shortage in academic literature,but indeed has drawn practical attention in the strategic planning process.To address the need,this paper presents an in-depth literature review of relevant studies that have identified different types of EV charging facilities,needs or concerns that are considered into EV charging demand and distribution modeling,alongside critical impacting factor identification,mathematical relationshipsof the contributing factorsandEVchargingdemand and distribution modeling.Key findings from the current literature are summarized with strategies for optimized plan of charging station deployments(i.e.,location and related number of charging station),in an attempt to provide a valuable reference for interested readers.展开更多
In order to understand how the uncertainties in the output can be apportioned to different sources of uncertainties in its inputs, it is critical to investigate the sensitivity of MOVES model. The MOVES model sensitiv...In order to understand how the uncertainties in the output can be apportioned to different sources of uncertainties in its inputs, it is critical to investigate the sensitivity of MOVES model. The MOVES model sensitivity for regional level has been well studied. However, the uncertainty analysis for project level running emissions has not been well understood. In this research, the MOVES model project level sensitivity tests on running emissions were conducted thru the analysis of vehicle specific power (VSP), scaled tractive power (STP), and MOVES emission rates versus speed curves. This study tested the speed, acceleration, and grade-three most critical variables for vehicle specific power for light duty vehicles and scaled tractive power for heavy duty vehicles. For the testing of STP, four regulatory classes of heavy duty vehicles including light heavy duty (LHD), medium heavy duty (MHD), heavy heavy duty (HHD) and bus were selected. MOVES project running emission rates were also tested for CO, PM2.5, NOx, and VOC versus the operating speeds. A Latin Hypercube (LH) sampling based on method for estimation of the "Sobal" sensitivity indices shows that the speed is the most critical variable among the three inputs for both VSP and STP. Acceleration and grades show lower response to the main effects and sensitivity indices. MOVES emission rates versus speeds curves for light duty vehicles show that highest emission occurs at lower speed range. No significant differences on emission rates among the regulatory classes of heavy duty vehicles are identified.展开更多
文摘Amassive market penetration of electric vehicles(EVs)associated with nonnegligible energy consumption and environmental issues has imposed a big challenge on evaluating electrical power distribution and related transportation facilities improvement in response to the largescale EV charging service need.Strategical deployment of EV charging stations including location and determination ofnumber of slowcharging stations and fast charging stationshas become an emerging concern and one of the most pressing needs in planning.This paper conducts a comprehensive survey of EV charging demand and distribution models with consideration of realistic driver behaviors impacts.This is currently a shortage in academic literature,but indeed has drawn practical attention in the strategic planning process.To address the need,this paper presents an in-depth literature review of relevant studies that have identified different types of EV charging facilities,needs or concerns that are considered into EV charging demand and distribution modeling,alongside critical impacting factor identification,mathematical relationshipsof the contributing factorsandEVchargingdemand and distribution modeling.Key findings from the current literature are summarized with strategies for optimized plan of charging station deployments(i.e.,location and related number of charging station),in an attempt to provide a valuable reference for interested readers.
基金support by U.S.Environmental Protection AgencyOhio Department of Transportation
文摘In order to understand how the uncertainties in the output can be apportioned to different sources of uncertainties in its inputs, it is critical to investigate the sensitivity of MOVES model. The MOVES model sensitivity for regional level has been well studied. However, the uncertainty analysis for project level running emissions has not been well understood. In this research, the MOVES model project level sensitivity tests on running emissions were conducted thru the analysis of vehicle specific power (VSP), scaled tractive power (STP), and MOVES emission rates versus speed curves. This study tested the speed, acceleration, and grade-three most critical variables for vehicle specific power for light duty vehicles and scaled tractive power for heavy duty vehicles. For the testing of STP, four regulatory classes of heavy duty vehicles including light heavy duty (LHD), medium heavy duty (MHD), heavy heavy duty (HHD) and bus were selected. MOVES project running emission rates were also tested for CO, PM2.5, NOx, and VOC versus the operating speeds. A Latin Hypercube (LH) sampling based on method for estimation of the "Sobal" sensitivity indices shows that the speed is the most critical variable among the three inputs for both VSP and STP. Acceleration and grades show lower response to the main effects and sensitivity indices. MOVES emission rates versus speeds curves for light duty vehicles show that highest emission occurs at lower speed range. No significant differences on emission rates among the regulatory classes of heavy duty vehicles are identified.