Instantaneous Fuel Consumption Models of Light Duty Vehicles and a Case Study on the Fuel Consumption at Different Traffic Conditions in Metro Manila Using Shepard’s Interpolation Method

2018 ◽  
Author(s):  
Ernesto B. Abaya ◽  
Karl B. Vergel ◽  
Edwin N. Quiros ◽  
Ricardo G. Sigua ◽  
Jose Bienvenido Biona
Keyword(s):  
Fuel Consumption ◽  
Interpolation Method ◽  
Traffic Conditions ◽  
Light Duty ◽  
Metro Manila ◽  
Light Duty Vehicles

The WLTC vs NEDC: A Case Study on the Impacts of Driving Cycle on Engine Performance and Fuel Consumption

2021 ◽  
Vol 18 (3) ◽  
pp. 9071-9081
Author(s):  
Jakub Lasocki
Keyword(s):  
Fuel Consumption ◽  
Combustion Engine ◽  
Engine Performance ◽  
Driving Cycle ◽  
Performance Characteristics ◽  
Pollutant Emission ◽  
Strong Impact ◽  
Light Duty ◽  
Light Duty Vehicles

The World-wide harmonised Light-duty Test Cycle (WLTC) was developed internationally for the determination of pollutant emission and fuel consumption from combustion engines of light-duty vehicles. It replaced the New European Driving Cycle (NEDC) used in the European Union (EU) for type-approval testing purposes. This paper presents an extensive comparison of the WLTC and NEDC. The main specifications of both driving cycles are provided, and their advantages and limitations are analysed. The WLTC, compared to the NEDC, is more dynamic, covers a broader spectrum of engine working states and is more realistic in simulating typical real-world driving conditions. The expected impact of the WLTC on vehicle engine performance characteristics is discussed. It is further illustrated by a case study on two light-duty vehicles tested in the WLTC and NEDC. Findings from the investigation demonstrated that the driving cycle has a strong impact on the performance characteristics of the vehicle combustion engine. For the vehicles tested, the average engine speed, engine torque and fuel flow rate measured over the WLTC are higher than those measured over the NEDC. The opposite trend is observed in terms of fuel economy (expressed in l/100 km); the first vehicle achieved a 9% reduction, while the second – a 3% increase when switching from NEDC to WLTC. Several factors potentially contributing to this discrepancy have been pointed out. The implementation of the WLTC in the EU will force vehicle manufacturers to optimise engine control strategy according to the operating range of the new driving cycle.

Powertrain Waste Heat Recovery: A Systems Approach to Maximize Drivetrain Efficiency

2012 ◽  
Author(s):  
Kevin Laboe ◽  
Marcello Canova
Keyword(s):  
Fuel Consumption ◽  
Waste Heat Recovery ◽  
Systems Approach ◽  
Waste Heat ◽  
Combustion Engine ◽  
Heat Energy ◽  
Engine Oils ◽  
Engine Cooling ◽  
Light Duty ◽  
Light Duty Vehicles

Up to 65% of the energy produced in an internal combustion engine is dissipated to the engine cooling circuit and exhaust gases [1]. Therefore, recovering a portion of this heat energy is a highly effective solution to improve engine and drivetrain efficiency and to reduce CO2 emissions, with existing vehicle and powertrain technologies [2,3]. This paper details a practical approach to the utilization of powertrain waste heat for light vehicle engines to reduce fuel consumption. The “Systems Approach” as described in this paper recovers useful energy from what would otherwise be heat energy wasted into the environment, and effectively distributes this energy to the transmission and engine oils thus reducing the oil viscosities. The focus is on how to effectively distribute the available powertrain heat energy to optimize drivetrain efficiency for light duty vehicles, minimizing fuel consumption during various drive cycles. To accomplish this, it is necessary to identify the available powertrain heat energy during any drive cycle and cold start conditions, and to distribute this energy in such a way to maximize the overall efficiency of the drivetrain.

scholarly journals Development of a fuel consumption measurement methodology for light duty vehicles in Colombia, based on metrology principles

DYNA ◽  
2020 ◽  
Vol 87 (212) ◽  
pp. 47-56
Author(s):  
Juan Carlos Castillo Herrera ◽  
Juan Camilo López Restrepo ◽  
David Andrés Serrato Tobón ◽  
Juan Esteban Tibaquirá Giraldo ◽  
Sergio Andrés Carvajal Perdomo
Keyword(s):  
Fuel Consumption ◽  
Fossil Fuels ◽  
The United States ◽  
Ambient Conditions ◽  
Road Transportation ◽  
Uncertainty In Measurement ◽  
Light Duty ◽  
Uncertainty Model ◽  
Measurement Methodology ◽  
Light Duty Vehicles

In this study, a methodology to measure fuel consumption for light duty vehicles (LDV) in Colombia was elaborated based on existing methodologies from road transportation worldwide. This methodology was proposed as a tool for the evaluation of energy efficiency strategies applied to vehicles, as well as establishing the baseline for measurement, control, and regulation of consumption of fossil fuels based on metrological criteria. Additionally, the capacities for measurement within Colombia were analyzed, and procedures stated by the Code of Federal Regulations of the United States of America were adopted for measuring fuel consumption of LDV by gravimetric methods. An uncertainty model based on the Guide to the expression of Uncertainty in Measurement (GUM) was elaborated, and the contribution of different variables associated to the measurement process the instruments, the equipment, and the ambient conditions over the uncertainty of the measurand, were analyzed.

scholarly journals Environmental effects of driving style: impact on fuel consumption

2019 ◽  
Vol 100 ◽  
pp. 00043 ◽  
Author(s):  
Jakub Lasocki ◽  
Karol Boguszewski
Keyword(s):  
Fuel Consumption ◽  
Strong Correlation ◽  
Quantitative Description ◽  
Ghg Emissions ◽  
Pollutant Emission ◽  
Mathematical Form ◽  
Combustion Engines ◽  
Driving Style ◽  
Light Duty ◽  
Light Duty Vehicles

From an environmental point of view, the fuel consumption of vehicles with combustion engines is directly related to the depletion of non-renewable crude oil resources and pollutant emission. The aim of this paper is to evaluate the effect of driving style on fuel consumption of light-duty vehicles. The study considered five metrics used for quantitative description of driving style: Dynamic Performance Index (DPI), Aggressiveness Factor (AF), Vehicle Aggressivity (VA), Total Aggressivity (TA), based upon the previous works of other researchers, and a newly proposed metric named Driving Style Indicator (DSI). All metrics were applied to the results of chassis dynamometer tests of two light-duty vehicles with spark-ignition and compression-ignition combustion engines. The values of metrics were plotted against corresponding values of fuel consumption to create dependences. Their analysis revealed that AF metric has strong correlation with fuel consumption, but is mathematically complex and requires numerous input data. DSI metric has simple mathematical form and is based only on the speed profile of the vehicle, and yet is characterized by a strong correlation with fuel consumption. DSI metric was further employed to investigate the influence of driving style on greenhouse gas (GHG) emissions from the Well-to-Wheel (WtW) perspective.

Development of Drive Cycles and Measurement of Fuel Economies of Light Duty Vehicles in Metro Manila

2018 ◽  
Author(s):  
Ernesto B. Abaya ◽  
Karl B. Vergel ◽  
Edwin N. Quiros ◽  
Ricardo Sigua
Keyword(s):  
Light Duty ◽  
Metro Manila ◽  
Light Duty Vehicles

Analysis of Driving Cycles for Emission Test of Light-Duty Vehicles in China

2012 ◽  
Vol 616-618 ◽  
pp. 1154-1160
Author(s):  
Jin Lin Xue
Keyword(s):  
Real World ◽  
Gasoline Engine ◽  
The Real ◽  
Driving Behaviors ◽  
Traffic Conditions ◽  
Driving Cycles ◽  
Driving Pattern ◽  
Light Duty ◽  
Light Duty Vehicles ◽  
The Impact

The driving cycles employed to measure the emissions from automotive vehicles should adequately represent the real-world driving pattern of the vehicle to provide the most realistic estimation of emissions levels. The driving cycles used for light-duty gasoline engine vehicles in China were reviewed in this paper firstly. Then the impact of various factors, such as driving behaviors, driving conditions, road conditions, traffic conditions, on real-world emission levels were analyzed. Finally, the shortages of the existing driving cycles were pointed out. It can be concluded that the emissions levels from automotive vehicles are underestimated because of the characteristics of the existing drive cycles, so it is urgent to research and develop new driving cycles to fit the situation of China.

scholarly journals Analysis and Prediction Model of Fuel Consumption and Carbon Dioxide Emissions of Light-Duty Vehicles

2022 ◽  
Vol 12 (2) ◽  
pp. 803
Author(s):  
Ngo Le Huy Hien ◽  
Ah-Lian Kor
Keyword(s):  
Carbon Dioxide ◽  
Fuel Consumption ◽  
Carbon Dioxide Emissions ◽  
Polynomial Regression ◽  
Control Strategies ◽  
Future Research ◽  
Light Duty ◽  
The Government ◽  
Government Of Canada ◽  
Light Duty Vehicles

Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications.

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