Fuel Consumption Of Vehicles Research Articles

Influence of Performance Packages on Fuel Consumption and Exhaust Emissions of Passenger Cars and Commercial Vehicles under WLTP

The transportation sector is responsible for about 16% of worldwide greenhouse gas emissions. Despite efforts for a sensible reduction by means of new technologies’ development, the average age of a vehicle fleet is 12.3 years in the European Union. In light of this, actions aiming at improving the efficiency of circulating vehicles can prove effective in the short to mid-term. Introducing performance packages in standard fuels could allow a reduction in the CO2 emissions of whole vehicle fleets without any modification to powertrain. Such a kind of additive is generally used in premium fuels; deposit control additives can reduce or control the deposits at intake valves and at nozzle holes with benefits for the fuel efficiency and exhaust emissions. Further improvements in combustion phasing can be achieved with cetane/octane improver. This paper aims to assess the influence of two performance packages on the exhaust emissions and fuel consumption of five vehicles set to be as representative as possible of circulating Italian passenger cars and light commercial fleet vehicles (LCVs). Based on the literature datasets, three Euro 4 vehicles were selected with a mileage representative of each single vehicle class: two passenger cars (one spark ignition and one diesel) and an LCV. Further, two diesel Euro 6 vehicles, a passenger car and an LCV, were tested to investigate the effect of fuel additives on the combustion of vehicles compliant with current homologation regulation. Exhaust emissions and fuel consumption were experimentally estimated on a chassis dynamometer over a worldwide harmonized light vehicles test cycle (WLTC) in a climate-controlled laboratory. Each vehicle was preliminarily tested when running with base fuel, then a 3000 km clean-up stage was performed using the additive package. Finally, WLTC tests were repeated. Results demonstrated the efficiency of the performance packages with a reduction between 1.2% (diesel Euro 6 passenger car) and 8.1% (diesel Euro 4 passenger car) in fuel consumption. Similar trends were found for CO2 emissions. Further, a sensible reduction in THCs, CO and PM was found for each vehicle class.

Mathematical Modeling of the Normal Fuel Consumption of Certain Vehicles

The scientific paper introduces original research and unique perspectives on the technical and economic aspects of fuel consumption in motor vehicles. Numerous experts argue that conducting practical experiments, specifically through active determinations, can yield noteworthy outcomes in this domain. To substantiate this viewpoint, the paper includes practical examples of experiments and considerations related to the mathematical model used for validating objective functions. These functions encompass the average fuel consumption in liters per 100 kilometers and the quantity of CO2 emitted per kilometer by the thermal engine of motor vehicles. The research also articulates the authors’ stance, citing the World Health Organization’s recommendation to decrease urban traffic speed from 50 km/h to 30 km/h. This reduction aims to mitigate traffic accidents and curb chemical noise and vibration pollution. The study amalgamates results from tests conducted on both urban and extra-urban routes, incorporating eco-friendly driving practices for comparative purposes. The primary focus of the mathematical model is to validate fuel consumption under real driving conditions. The paper concludes with insights and future research directions in modeling.

Analysis and Optimization of the Fuel Consumption of an Internal Combustion Vehicle by Minimizing the Parasitic Power in the Cooling System

This study aims to enhance energy efficiency by reducing parasitic losses in the engine cooling system through a new drive strategy involving a two-stage water pump and a variable electro-fan. The fuel consumption gain analysis focused on a vehicle with average characteristics typical of 1.0L hatchbacks in the Brazilian market and urban driving conditions. The methodology implemented aims to minimize power absorbed by the forced water circulation and thermal rejection, thereby reducing parasitic losses, particularly during low-speed urban driving, without causing air-side heat exchanger saturation. The results show a potential decrease of up to 80% in power absorbed by the cooling system, leading to an estimated fuel consumption saving of approximately 1.4% during urban driving cycles.

Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle’s fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results.

Effect of Tire Inflation Pressure and Wheel Front Toe Angle on Fuel Consumption of a Light-Duty Vehicle

Effect of Tire Inflation Pressure and Wheel Front Toe Angle on Fuel Consumption of a Light-Duty Vehicle

Reducing the Fuel Consumption of an Hybrid Electric Vehicle With the Use of Model Predictive Control-Case Study

The article presents operational aspects related <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">to energy management strategy (EMS) - model predictive control (MPC). The mathematical model showing the synergy of working machines of the hybrid electric vehicle (HEV) was presented. Then the results of road tests have been shown. They were based on the factory control of the above-mentioned system. The results of the operating parameters of the system according to the factory control and the results of the operating parameters according to the MPC with different linear quadratic tracking (LQT) curves were compared. Application of thirteen reference LQT trajectories results in lower fuel consumption of the HEV. It led to changes in the power and the battery state of charge (SOC): for factory control - from 50.1% (the beginning) to 56.1% (the end of course) and for MPC from 50.1% (the beginning) to 59.9% (the end of the course). The applied MPC with 13 reference trajectories (LQT) of power machines of the series-parallel HEV allowed for fuel savings on the level of 4%.

Influence of the Use of EtG Synthetic Fuel in Spark-Ignition Engines on Vehicle Fuel Consumption and Pollutant Emissions

This article presents the properties of EtG synthetic fuel (Ethanol to Gasoline) as a substitute for fuels in an installation using ethanol from food waste as a raw material. The results of this research on the process of supplying the engine with EtG fuel are presented, in which the operational suitability of this fuel was verified, including environmental requirements. The results of pollutant emission tests from vehicles with spark-ignition engines in dynamic driving conditions in the New European Driving Cycle (NEDC) type approval tests and the World Harmonized Test Cycle (WLTC) test, both on a “cold” and “hot engine”, are presented. It was found that EtG fuel is characterized by lower emissions of carbon monoxide, nitrogen oxides, and methane, and higher emissions of non-methane hydrocarbons compared to E10 commercial gasoline, while the specific distance emission of carbon dioxide for both fuels was very similar.

An Optimisation Technique with the Method of Construction for Vehicle Fuel Consumption and Emissions Using Incomplete Block Designs with Some Special Types of Graphs

Background: The main key input variables to this optimization technique for constructing incomplete block designs are using bipartite and spanning subgraphs through numerical examples of vehicle fuel consumption and emissions. The theory of graphs plays a significant role in mathematical sciences and engineering technologies. In addition, the graph models many relations and processes in physical, biological, social, and information systems. Aims and Objectives: The construction methods using Partially Incomplete Block Designs (PBIBD) with differential equations through bipartite and spanning subgraphs that predict hot stabilized vehicle fuel consumption and emission rates for different drivers using different cars are studied in this paper. The other modelling of fuel consumption and emissions have appeared as an essential tool, which helps to develop and measure vehicle techniques and to help estimate vehicle fuel consumption and emissions. This paper aims to develop an optimization technique for the construction method for incomplete block designs LSD with PBIBD(2) through vehicle fuel consumption and emissions. Method: An incomplete block design can be constructed using LSD statistical analysis with bipartite and spanning subgraphs. First, the method for the construction of LSD using bipartite graphs. The second method for the construction of PBIBD(m) using spanning subgraphs. The two construction methods are through numerical examples of an oil company testing five mixings of petrol for fuel efficiency and emission according to the variability of five drivers and five models of cars. Result: The inference of the first model of PBIBD(2) using LSD F-value of 0.08 implies the model is not significant (P-values greater than 0.05). The second model has no significant difference between petrol fuel efficiency and emissions. In the third model, there is no significant difference in fuel efficiency between different cars of petrol bunks. Conclusion: Finally, it is concluded that the response variable is represented above the maximum quality scores from our fourth driver using a second car to the fourth petrol bunk in fuel efficiency.

Predictability of Vehicle Fuel Consumption Using LSTM: Findings from Field Experiments

Predictability of Vehicle Fuel Consumption Using LSTM: Findings from Field Experiments

Backward-Facing Analysis for the Preliminary Estimation of the Vehicle Fuel Consumption

In this paper, a methodology for the estimation of fuel consumption using backward-facing analysis is presented. The method for the determination of fuel consumption was based on the evaluation of the total work required to drive the vehicle along a specific drive cycle. At the same time, the potential fuel economy was estimated using the energy that can be harvested from the wheel. The results obtained using this model were compared with complex MATLAB/Simulink models developed using the forward-facing strategy. The MATLAB Simulink model details conventional and hybrid vehicle models capable of estimating fuel consumption. The components of the longitudinal forces opposing the vehicle during driving were investigated and analyzed using the results of the analytical and simulation models. The procedure can be successfully applied to provide a quick estimate of the fuel consumption performance before detailing complex simulation models.

Mechanistic Excess Fuel Consumption of a 3D Passenger Vehicle on Rough Pavements

A passenger vehicle’s fuel-powered engine compensates for its dissipated energy, which is caused by road roughness. This paper presents an integrated vehicle–pavement approach and investigates the excess fuel consumption of a seven-degree-of-freedom (DOF) full-car model on rough pavements. In the approach, a nonstationary Laplace process is used to artificially generate road roughness profiles with consideration of local roughness variance and parallel roughness correlation. The proposed mechanistic approach quantifies the impact of road roughness and vehicle dynamic characteristics on excess fuel consumption. The study concluded that overlooking local roughness variance, an indicator of road roughness nonstationarity, may underestimate excess fuel consumption by 30%. Compared with a two-dimensional (2D) half-car model, a three-dimensional (3D) full-car model reduces computation error of excess fuel consumption by approximately 11%. For practical implementation, the extended roughness-speed-impact (ERSI) model (R2=98%) is developed to estimate roughness-induced energy dissipation of five two-axle vehicles.

Study on Vehicle Fuel Consumption and Exhaust Emissions Based on a New Viscous Macroscopic Traffic Flow Model

Based on a conserved higher-order traffic flow model (CHO model), we propose a new viscous macroscopic traffic flow model taking into account the diffusion effect in traffic. The model can reasonably smooth the shock wave so that the acceleration is maintained in a reasonable range. To balance the computational efficiency and accuracy, this viscous macroscopic traffic flow model is integrated with the microscopic vehicle fuel consumption and emission models to estimate vehicle fuel consumption and exhaust emissions. The local discontinuous Galerkin (LDG) method is used to solve the viscous model, and the simulation results are inputted into the microscopic models to calculate vehicle fuel consumption and exhaust emissions. Numerical results illustrate that the proposed viscous model is reasonable and that the designed scheme is feasible and effective. Moreover, we provide concrete suggestions for controlling vehicle fuel consumption and exhaust emissions.

Optimization of Engine Control Strategies for Low Fuel Consumption in Heavy-Duty Commercial Vehicles

Optimization of Engine Control Strategies for Low Fuel Consumption in Heavy-Duty Commercial Vehicles

Modelling, Simulation and Validation of Hybrid Vehicle Fuel Consumption

Modelling, Simulation and Validation of Hybrid Vehicle Fuel Consumption

Monitoring Vehicle Pollution and Fuel Consumption Based on AI Camera System and Gas Emission Estimator Model.

Road traffic is responsible for the majority of air pollutant emissions in the cities, often presenting high concentrations that exceed the limits set by the EU. This poses a serious threat to human health. In this sense, modelling methods have been developed to estimate emission factors in the transport sector. Countries consider emission inventories to be important for assessing emission levels in order to identify air quality and to further contribute in this field to reduce hazardous emissions that affect human health and the environment. The main goal of this work is to design and implement an artificial intelligence-based (AI) system to estimate pollution and consumption of real-world traffic roads. The system is a pipeline structure that is comprised of three fundamental blocks: classification and localisation, screen coordinates to world coordinates transform and emission estimation. The authors propose a novel system that combines existing technologies, such as convolutional neural networks and emission models, to enable a camera to be an emission detector. Compared with other real-world emission measurement methods (LIDAR, speed and acceleration sensors, weather sensors and cameras), our system integrates all measurements into a single sensor: the camera combined with a processing unit. The system was tested on a ground truth dataset. The speed estimation obtained from our AI algorithm is compared with real data measurements resulting in a 5.59% average error. Then these estimations are fed to a model to understand how the errors propagate. This yielded an average error of 12.67% for emitted particle matter, 19.57% for emitted gases and 5.48% for consumed fuel and energy.

Electrification of Vehicle Miles Traveled and Fuel Consumption within the Household Context: A Case Study from California, U.S.A.

Plug-in electric vehicles (PEVs), consisting of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), combined with the decarbonization of the electricity sector, can significantly help reduce greenhouse gas emissions in the transport sector. This study used empirical data from 287 households with at least one plug-in electric vehicle in California between 2016 and 2020. We estimated electric vehicle miles traveled (eVMT), fuel consumption and utility factor at the household level, i.e., taking into consideration all vehicles. We also studied the effect of household-specific factors—such as frequency of overlaps between vehicles, frequency of charging and frequency of long-distance trips—on eVMT, utility factor and fuel consumption within two-car households. Our results indicate that PHEVs with a range of at least 35 miles have the potential to electrify a similar share of total household miles as some short range BEVs, or can reach up to 70% as much electrification as some long range BEVs and, thus, can play an important role in decarbonizing the transport sector.

Modelling Vehicle Emissions and Fuel Consumption Based on Instantaneous Speed and Acceleration Levels

Modelling Vehicle Emissions and Fuel Consumption Based on Instantaneous Speed and Acceleration Levels

Effect of Gear Usage on the Fuel Consumption of Light Duty Vehicle by Using On-Board Diagnostics (OBD) DataEffect of Gear Usage on the Fuel Consumption of Light Duty Vehicle by Using On-Board Diagnostics (OBD) Data

Transmission gear ratio usually affects transportation fuel usage during travelled on road. The manner of gear usage is one of the main factors affecting the fuel consumption of vehicles. By using the neutralizing driving data collection, the vehicle Toyota Hilux 2.5 liters has been used to conduct the experiment to determine the fuel consumption, based on the average vehicle speed, engine speed and gear usage. Some parameters were investigated, with a special emphasis on the relation between the engine speeds and vehicle speeds, also presenting an example of using the information provided by the OBD to determine the gear ratios. In the end, the gear steps and the way they are used while driving the vehicle were identified. It is found that each gear usage can have a specific impact on fuel consumption and the usage of first gear indicates higher average fuel consumption with 60.5% with 35.8 liters/100km compared with second gear. In conclusion, the fuel consumption is significantly determined by a proper gear selection

Characterization of Gear Usage to Fuel Consumption of a Light Duty Vehicle from Direct Measurements by Using On-Board Diagnostics (OBD) Data

Transportation contributes a major share of the fuel usage from total vehicles in developing countries. The manner of gear usage is one of the main factors affecting the fuel consumption of motor vehicles. The presented results came from the tests conducted on Toyota Hilux 2.5 liters vehicle to determine the fuel consumption, based on the average vehicle speed, engine speed and gear usage collected from using the neutralizing driving data collection. Some parameters were investigated, with a special emphasis on the relation between the engine speeds and vehicle speeds, also presenting an example of using the information provided by the OBD to determine the gear ratios. In the end, the gear steps and the way they are used while driving the vehicle were identified. It is found that each gear usage can have a specific impact on fuel consumption and the lowest fuel consumption recorded under fifth gear with 9.75 liters/100km which reduction of 4.3% reduction compared with 4th gear. In conclusion, the fuel consumption is significantly determined by a proper gear selection.

Investigation of an efficient model-based fuel economy optimization methodology for diesel engines focusing on real world driving emission cycles

Together with stringent emission standards, meeting fuel consumption, and performance targets have made engine calibration process complicated, lengthy, and costly. For this reason, it is crucial to develop an efficient methodology for the optimization of a diesel engine calibration. In this study, a novel automated fine tuning methodology using genetic algorithms and data driven models is proposed. Fuel consumption of a light commercial vehicle with 2.0-L diesel engine was optimized for a predefined Real Driving Emissions (RDE) cycle. During the optimization, tail pipe emission limits were maintained by taking the aftertreatment system efficiency into account. To this end, a representative engine control unit, internal combustion engine, and Selective Catalyst Reduction (SCR) system models were built by using empirical data and neural networks. Moreover, the effect of choosing different tailored optimization points for a predefined route was investigated by comparison of frequency and fuel consumption based weighting. The cost function, related to the optimization problem, was defined based on fuel consumption, performance, and mechanical limits of the engine, and in accordance with tailpipe emission limits defined by Euro 6d-Temp regulations. Compared to previous model-based calibration studies, tailpipe emissions, and regulation limits have been incorporated in this study in order to obtain more realistic calibrations. After testing different genetic algorithm configurations, the fuel efficiency over the predefined route was improved by 3.07% without violating any of the limits while the smoothness of the calibration maps was also preserved. This route specific calibration methodology is believed to have the potential to increase fuel economy in real world applications such as daily commuting routes or repetitive driving patterns for instance, operation vehicle use cases in factories, airports, and construction areas. As for further development, this can be achieved by running simulations and the optimization on cloud and updating the calibration remotely based on the selected route before the trip.