Impact of Intelligent Transportation Systems on Vehicle Fuel Consumption and Emission Modeling: An Overview

Faris, Waleed Fekry; Rakha, Hesham A.; Elmoselhy, Salah

doi:10.4271/2013-01-9094

Cited by 13 publications

(34 citation statements)

References 27 publications

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“…Diesel engines power most of the trucks, buses, trains, ships, and off-road machinery. Pollutants emitted by them significantly affect climate change [2,3]. Among the drawbacks of diesel engines is that fuel droplets don't combust quickly/completely compared to a premixed charge, with a sudden change of engine speed.…”

Section: Introductionmentioning

confidence: 99%

Experimentally validated analytical modeling of diesel exhaust HC emission rate

2014

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Supercharged diesel engines are a key source of hazardous regulated emissions that have been extensively modelled, yet without explainable mathematical trends. The present paper demonstrates the analytical modeling of the percentage of unburned Hydrocarbon and the HC emission rate in four-stroke diesel engines for trucks. The study presents as well the analytical modeling of the supercharged air density. A sensitivity analysis has been conducted on these developed models. The study shows that the average percentage of deviation of the simulated results from the corresponding freeway cycles field data on the percentage of unburned Hydrocarbon and the HC emission rate is 10.6% and 4%, respectively. The corresponding coefficient of determination is 70% and 83%, respectively. The relative error of the developed models of the percentage of unburned Hydrocarbon and the HC emission rate is 10.6% and 2%, respectively. The study demonstrates with 99% coefficient of determination that the average percentage of deviation of the simulated results from the corresponding field data under the steady speed operating condition for all freeway cycles on the supercharged air density is 3.7%. The relative error of the developed model of the supercharged air density is 4%. These values of relative error are in an order of magnitude of deviation that is less than that of widely recognized models in the field of vehicle powertrain modeling, such as the CMEM. These developed analytical models serve as widely valid models that follow entirely from the principles of physics and the results of these developed models have explainable mathematical trends. The fact that these developed models are dimensionally correct further supports the validity of these models. The present models can help in better analyzing the performance of diesel engines and in developing and assessing the performance of these engines.

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Section: Introductionmentioning

confidence: 99%

Experimentally validated analytical modeling of diesel exhaust HC emission rate

2014

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“…Thus, modeling methods usually simplify the vehicle's energy-consumption characteristics by using statistical approaches and neglecting several details of the fundamental powertrain operation. One of the most conventional approaches throughout the past decades of research is to estimate instantaneous driving power and then establish the relationship between driving power and fuel-consumption rate [5].…”

Section: Introductionmentioning

confidence: 99%

“…The VSP equation has been incorporated in several fuel-consumption models, including the motor-vehicle emission simulator (MOVES) released by the United States Environment Protection Agency (EPA) [5,7]. Jiménez et al also suggested a set of specific coefficients for light-duty-vehicle (LDVs) applications.…”

Section: Introductionmentioning

confidence: 99%

“…A number of studies have reported high correlation between VSP and fuel consumption. Furthermore, the most prominent advantage of VSP is that it only requires externally measurable parameters, vehicle speed, acceleration, road grade, and wind speed, to calculate driving power [5,[8][9][10]. These parameters can be acquired by a global positioning system (GPS), inertia measurement unit (IMU), and wind-speed sensor, of which the installation does not require any additional vehicle modifications.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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This study is the first of a two-part paper. The overall study presents a new methodology to improve the accuracy of hybrid vehicles’ energy-consumption model over conventional transportation modeling methods. The first paper attempts to improve an equation for vehicles’ driving-power estimation to be more realistic and specific for a particular vehicle model or fleet. The second paper adopts the driving-power equation to estimate the requested driving power. Then, the data are utilized to construct the hybrid-vehicle energy-consumption model, namely, the traction-force–speed-based energy-consumption model (TFS model). The main concept of the first paper is to utilize the power-split hybrid powertrain’s accessible on-board diagnostics (OBD) dataset, and its dynamic model to estimate the total propulsion power. Then, propulsion power was applied as the main parameter for driving-power equation development and vehicle-specific coefficient calibration. For coefficient calibration, this study implemented the stepwise multiple regression method to select and calibrate an optimal set of coefficients. Results showed that conventional driving-power equations Vehicle-Specific Power (VSP) LDV 1999 and VSP Prius3Spec provide low prediction fidelity, especially under high-speed (>80 km/h) and heavy-load driving (≥50 kW). In contrast, D r v P w P r i u s 3 , proposed in this study, effectively improved prediction to become more accurate and reliable through all driving conditions and speed ranges. It dramatically helped to reduce prediction discrepancy over the conventional equations at heavy-load driving, from an R-square of 0.79 and 0.78 to 0.96. D r v P w P r i u s 3 also the prediction error at high-speed driving from the maximal error of approximately −20 to −5 kW. This study also discovered that aerodynamics and rolling resistance were the primary factors that caused the prediction error of conventional VSP equations. In addition, results in this study showed that both of the approaches used to establish the P P T d r v and D r v P w P r i u s 3 equations were valid for a power-split hybrid vehicle’s driving-power estimation. For the coefficient-calibration part, the stepwise and multiple regression method is low-cost and simple, allowing to calibrate an appropriate set of optimal coefficients for a specific vehicle model or fleet.

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“…In this context, for lowering the energy consuming in public bus transportation sector, both the governing bodies, manufacturers and also researchers are using vehicle fuel consumption models that can be implemented in traffic simulations which estimates real world operation data. These simulation methodologies were classified into two main categories by Faris et al [6] . These are: (a) Scale of Input Variable-based modelling (SIVM), and (b) Formulation Approach-based Modelling (FAM).…”

Section: Introductionmentioning

confidence: 99%

A fuel consumption model for public transportation with 3-D road geometry approach

Özener¹,

Özkan²,

Orak³

et al. 2018

THERM SCI

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Public transportation fuel consumption modeling shows a great importance because of economic and environmental aspects. Considering to the metropolises with millions of inhabitants with circulating thousands of buses that uses intercity lines, its importance is becoming vital in planning both operation of transportation company and city mobility planning. In this context a detailed fuel consumption modelling approach for public transportation buses was used for vehicle fuel consumption assessment during operation. The methodology was developed with IPG TruckMaker + AVL Cruise co-simulation environment following instantaneous speed, load and 3-D road data primarily besides model parameters. The model was validated at one of the most important public transportation axle of the world, Istanbul Metrobus System, at two direction which carries over ~1 million passenger daily in 24 hours operation with petrol engine buses. The comparison simulation/measurements showed that the proposed fuel consumption model is accurate and can predict fuel consumption behavior for public transit buses in a reliable band. In addition, this methodology can be used to investigate various powertrain and operating scenarios near future for more efficient public transportation with high reliability.

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Impact of Intelligent Transportation Systems on Vehicle Fuel Consumption and Emission Modeling: An Overview

Cited by 13 publications

References 27 publications

Experimentally validated analytical modeling of diesel exhaust HC emission rate

Experimentally validated analytical modeling of diesel exhaust HC emission rate

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

A fuel consumption model for public transportation with 3-D road geometry approach

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