Can UK passenger vehicles be designed to meet 2020 emissions targets? A novel methodology to forecast fuel consumption with uncertainty analysis

Vehicle manufacturers are required to reduce their European sales-weighted emissions to 95 g CO2/km by 2020, with the aim of reducing on-road fleet fuel consumption. Nevertheless, current fuel consumption models are not suited for the European market and are unable to account for uncertainties when used to forecast passenger vehicle energy-use. Therefore, a new methodology is detailed herein to quantify new car fleet fuel consumption based on vehicle design metrics. The New European Driving Cycle (NEDC) is shown to underestimate on-road fuel consumption in Spark (SI) and Compression Ignition (CI) vehicles by an average of 16% and 13%, respectively. A Bayesian fuel consumption model attributes these discrepancies to differences in rolling, frictional and aerodynamic resistances. Using projected inputs for engine size, vehicle mass, and compression ratio, the likely average 2020 on-road fuel consumption was estimated to be 7.6 L/100 km for SI and 6.4 L/100 km for CI vehicles. These compared to NEDC based estimates of 5.34 L/100 km (SI) and 4.28 L/100 km (CI), both of which exceeded mandatory 2020 fuel equivalent emissions standards by 30.2% and 18.9%, respectively. The results highlight the need for more stringent technological developments for manufacturers to ensure adherence to targets, and the requirements for more accurate measurement techniques that account for discrepancies between standardised and on-road fuel consumption

Fuel Consumption Tabulation in Laboratory Conditions

Environmental degradation has come about for a number of factors including the use of fossil fuels in vehicles for everyday use. This paper attempts to understand the relationship between fuel consumption and various engine performance parameters under laboratory conditions in order to see how various factors contribute to the overall fuel consumption. The framework for testing has been decided as the New European Drive Cycle (NEDC) given its various testing advantages against other driving cycles. A test rig was applied to simulate the NEDC under laboratory conditions. The findings from this study provide information how vehicular fuel consumption varies with such driving parameters as vehicle speed, acceleration, and throttle position. They can be used to predict fuel consumption under any real life driving conditions, which will contribute to reducing fuel consumption in future vehicle desig

Real-world comparison of probe vehicle emissions and fuel consumption using diesel and 5 % biodiesel (B5) blend.

An instrumented EURO I Ford Mondeo was used to perform a real-world comparison of vehicle exhaust (carbon dioxide, carbon monoxide, hydrocarbons and oxides of nitrogen) emissions and fuel consumption for diesel and 5% biodiesel in diesel blend (B5) fuels. Data were collected on multiple replicates of three standardised on-road journeys: (1) A simple urban route; (2) A combined urban/inter-urban route; and, (3) An urban route subject to significant traffic management. At the total journey measurement level, data collected here indicate that replacing diesel with a B5 substitute could result in significant increases in both NOx emissions (8-13%) and fuel consumption (7-8%). However, statistical analysis of probe vehicle data demonstrated the limitations of comparisons based on such total journey measurements, i.e., methods analogous to those used in conventional dynamometer/drive cycle fuel comparison studies. Here, methods based on the comparison of speed/acceleration emissions and fuel consumption maps are presented. Significant variations across the speed/acceleration surface indicated that direct emission and fuel consumption impacts were highly dependent on the journey/drive cycle employed. The emission and fuel consumption maps were used both as descriptive tools to characterise impacts and predictive tools to estimate journey-specific emission and fuel consumption effects

Drive Cycle Optimisation for Pollution Reduction

Green house gas emissions have abraded environmental quality for human existence. Automobile exhaust is a significant contributor globally to green house gases, among other contributors. This research investigates how vehicle fuel consumption can be tabulated from laboratory tests and road tests. The laboratory tests are used to establish mathematical relationships to predict fuel consumption as a function of such drive-cycle parameters as vehicle speed,acceleration and throttle position. Then, these relationships are applied to calculate fuel consumption during real-life road tests. In the future, the drive-cycle parameters contributing to vehicle fuel consumption could be optimized to lower automobile exhaust’s impact on environmental degradation

Predicting fuel energy consumption during earthworks

This research contributes to the assessment of on-site fuel consumption and the resulting carbon dioxide emissions due to earthworks-related processes in residential building projects, prior to the start of the construction phase. Several studies have been carried out on this subject, and have demonstrated the considerable environmental impact of earthworks activities in terms of fuel consumption. However, no methods have been proposed to estimate on-site fuel consumption during the planning stage. This paper presents a quantitative method to predict fuel consumption before the construction phase. The calculations were based on information contained in construction project documents and the definition of equipment load factors. Load factors were characterized for the typical equipment that is used in earthworks in residential building projects (excavators, loaders and compactors), taking into considering the type of soil, the type of surface and the duration of use. We also analyzed transport fuel consumption, because of its high impact in terms of pollution. The proposed method was then applied to a case study that illustrated its practical use and benefits. The predictive method can be used as an assessment tool for residential construction projects, to measure the environmental impact in terms of on-site fuel consumption. Consequently, it provides a significant basis for future methods to compare construction projects.Peer ReviewedPostprint (author's final draft

Heavy Duty Vehicle Fuel Consumption Modelling Using Artificial Neural Networks.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly, a transient state test was performed, in order to evaluate the polynomial regression and 25 ANN models with different parameters. Based on the results, the best ANN model was chosen. Then, validation test was conducted using real duty cycle loads for model comparison. The neural network model outperformed the conventional method and represents fuel consumption of the engine operating in transient states significantly better. The presented method can be applied in order to reduce fuel consumption in utility vehicles delivering accurate fuel economy model of truck engines, in particular in low engine speed and torque range

Enhancing fuel burning efficiency by regulate spark plug voltage supply

Nowadays, fuel consumption, fuel efficiency and fuel economy is number one priority for people. The need to reduce fuel consumption is because oil reserves are running low and the cost of gasoline will only increase day by day. The benefits of increased fuel economy is significant energy, cost saving, reducing greenhouse emission, improved air quality and etc. To increase the fuel efficiency, many fuel efficient vehicles was created. Fuel-efficient vehicles are extremely important because we need to cut our fuel consumption and find other way of powering cars. Thus, this research introduced the system that can reduce fuel consumption and improve car performance when the car battery voltage is increase. This system use ac power supply replacing the car battery to get the dc power supply. Also, the system enables to increase voltage from 12 volt to 16 volt supplied to spark plug to enhance fuel burning efficiency. Aims of project are to improve car performance and reduce fuel consumption. To meet the desired aim of this research, the experiment to measure car performance using power performance that are produce from current voltage and the experiment similarly concept with combustion process was done. From that experiment, the system had improved car performance and lessing fuel consumption to 33% when the voltage increases to 16V

Improvements in petrol engine performance with ultrasonic fuel atomisation

Initial studies of the effect of air-fuel mixture preparation on piston engine performance have been conducted on a four cylinder 1600 cc petrol engine using conventional carburation and ultrasonic fuel atomisation. The performance of the engine, under various conditions of operation, has been assessed on the basis of specific fuel consumption and brake mean effective pressure. Whereas only minor differences in performance were found under full power condition at part throttle running of the engine with ultrasonic fuel atomisation improvements in fuel consumption in excess of 10% were observed. These improvements appear to be the direct result of better mixture preparation. Indirect benefits of improved mixture preparation may be a reduction in exhaust smoke and hydrocarbon emission from the engine