Comparison of Vehicle-Specific Fuel Use and Emissions Models Based on Externally and Internally Observable Activity Data

Abstract

On-road vehicles consume a substantial amount of fuel and emit air pollutants, including carbon monoxide, hydrocarbon, and nitrogen oxides. Increasing concern about air quality has meant increasing interest in accurate estimates of microscale vehicle energy use and emissions (EU&E). Vehicle EU&E rates are related to vehicle-specific power (VSP). For a given vehicle, VSP is a function of vehicle speed, acceleration, and grade, each of which can be categorized as an externally observable variable (EOV). However, internally observable variables (IOVs), such as manifold absolute pressure (MAP) and engine revolutions per minute (RPM), are more predictive of EU&E. Field measurements were conducted for 10 on-road vehicles using a portable emissions measurement system, an on-board diagnostic scan tool, and GPS receivers to measure exhaust concentrations and engine activity and to estimate road grade. VSP and the product of MAP and RPM ( PM×R) are useful as indicators of engine load. IOV-based models were developed to predict EU&E rates. The IOV-based models typically performed better than EOV-based models. The models demonstrated could be integrated into next-generation traffic simulation programs, as well as into vehicle engine control units, to provide feedback on real-time emissions.

Development of Hybrid-Vehicle Energy-Consumption Model for Transportation Applications—Part I: Driving-Power Equation Development and Coefficient Calibration

“…1 seemed to provide a good balance between prediction accuracy and calculation complexity. The newly calibrated driving-power equation is expressed as Equation (14). To some extent, results indicated that applying complex rolling-resistance terms mgcosθC r (C 1 v veh 2 + C 2 v veh ), and the other road-load term, β 1 v veh 3 + β 2 v veh 2 + β 3 v veh , included in Indt var.…”

“…Figure 10 shows the comparison of Prius3 s driving energy utilization between city and highway driving. Data were classified into each contributor term: acceleration, road grade, aerodynamic, and rolling resistance, which were estimated by the terms in Equation (14). In the city, more than 70% of energy was utilized for acceleration, which mostly took place during a stop-and-go situation.…”

“…Since 1999, a great number of studies have applied the conventional VSP equation and coefficients for LDV to estimate the driving power of passenger cars regardless of vehicle model and technology. VSP has been utilized to estimate driving power to construct fuel-consumption and -emission models, and conduct further analysis on the influential factors or contributors to vehicle fuel consumption [1,3,[7][8][9][10][11][12][13][14][15][16][17][18][19]. Nevertheless, before implementing the VSP equation and the LDV coefficients, none of them has performed validation to confirm prediction accuracy of whether the equation and coefficients estimated by the vehicles from the last 20 years can still accurately capture a vehicle or studied vehicle fleet.…”

Geospatial Variation of Real-World Tailpipe Emission Rates for Light-Duty Gasoline Vehicles

“…Speed, acceleration, RG, and vehicle specific power (VSP) are typically the largest sources of variability in 1 Hz emission rates of an individual vehicle. − VSP, an indicator of engine power demand, is a function of second-by-second speed, acceleration, and RG . Average positive acceleration explains some variability in emission rates. , Average positive VSP is an indicator of fuel consumption. Mass per time emission rates typically increase monotonically with positive VSP. , …”

“…Average positive acceleration explains some variability in emission rates. , Average positive VSP is an indicator of fuel consumption. Mass per time emission rates typically increase monotonically with positive VSP. , …”