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The energy consumption of a vehicle is typically determined either by testing or in simulation. While both approaches are valid, they only work for a specific drive cycle, they are time intensive, and they do not directly result in a closed-form relationship between key parameters and consumption. This paper presents an alternative approach that determines the consumption based on a simple analytical model of the vehicle and statistical parameters of the drive cycle, specifically the moments of the velocity. This results in a closed-form solution that can be used for analysis or synthesis. The drive cycle is quantified via its moments, specifically the average speed, the standard deviation of the speed as well as the higher order moments skewness, and the kurtosis. A mixed quadratic term is added to account for acceleration or aggressiveness, but it is noticeably distinct from the conventional metric of positive kinetic energy (PKE).

Significant exhaust enthalpy is wasted in gasoline turbocharged direct injection (GTDI) engines; even at moderate loads the WG (Wastegate) starts to open. This action is required to reduce EBP (Exhaust Back Pressure). Another factor is catalyst protection, placed downstream turbine. Lambda enrichment is used to perform this. However, the conventional turbine has a temperature drop across it when used for energy recovery. Catalyst performance is critical for emissions, therefore the only location for any additional device is downstream of it. This is a challenge for any additional energy recovery, but a smaller turbine is a design requirement, optimised to work at lower operating pressure ratios. A WAVE model of the 2.0L GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated with steady state dynamometer data to estimate drive cycle benefit.

This paper presents the implementation of a vehicle and powertrain model of the parallel hybrid electric vehicle which can be used for several purposes: as a model for estimating fuel consumption, as a model for estimating performance, and as a control model for the hybrid powertrain optimisation. The model is specified as a multi-domain physical model in MATLAB Simscape, which captures the key electrical, mechanical and thermal energy flows in the vehicles. By applying hand crafted boundary conditions, this model can be simulated either in the forwards or backwards direction, and it can easily be simplified as required to address specific control problems. Modelling in the forwards direction, the driver inputs are specified, and the vehicle response is the model output. In the backwards direction, the vehicle velocity as a function of time is the specified input, and the engine torque, and fuel consumption are the model outputs.

Panel Discussions held at the SAE World Congress in both 2013 and 2014 observed that a shortage of good quality engineering talent formed a chronic and major challenge. (“Good quality” refers to applicants that would be shortlisted for interview.) While doubts have been expressed in some quarters, the shortage is confirmed by automotive sector employers and the Panel's view was that it was symptomatic of a range of issues, all of which have some bearing on the future of the profession. Initiatives to improve recruitment and retention have had varying degrees of success. Efforts need to be intensified in primary schools where negative perceptions develop and deepen. Schemes like AWIM that operate on a large scale and are designed to supplement school curricula should operate at an international level. Universities represent the entry point into the engineering profession and their role in the recruitment process as well as education and training is crucial.

Recent changes to the rules regarding aerodynamics within Formula SAE, combined with faster circuits at the European FSAE events, have made the implementation of aerodynamic devices, to add down-force, a more relevant topic. As with any race series it is essential that a detailed analysis is completed to establish the costs and benefits of including an aerodynamic package on the vehicle. The aim of the work reported here was to create a methodology that would fully evaluate all aspects of the package and conclude with an estimate of the likely gain in points at a typical FSAE event. The paper limits the analysis to a front and rear wing combination, but the approach taken can be applied to more complex aerodynamic packages.

The use of simulation tools by vehicle manufacturers to design, optimize and validate their vehicles is essential if they are to respond to the demands of their customers, to meet legislative requirements and deliver new vehicles ever more quickly. The use of such tools in the aerodynamics community is already widespread, but they remain some way from replacing physical testing completely. Further advances in simulation capabilities depend on the availability of high quality validation data so that simulation code developers can ensure that they are capturing the physics of the problems in all the important areas of the flow-field. This paper reports on an experimental program to generate such high quality validation data for a SAE 20 degree backlight angle notchback reference model.

In order to identify predictive models for a diesel engine combustion process, combustion cylinder pressure together with other fuel path variables such as rail pressure, injector current and sleeve pressure of 1000 continuous cycles were sampled and collected at high resolution. Using these engine steady state test data, three types of modeling approach have been studied. The first is the Auto-Regressive-Moving-Average (ARMA) model which had limited prediction ability for both peak combustion pressure (Pmax) and Indicated Mean Effective Pressure (IMEP). By applying correlation analysis, proper inputs were found for a linear predictive model of Pmax and IMEP respectively. The prediction performance of this linear model is excellent with a 30% fit number for both Pmax and IMEP. Further nonlinear modeling work shows that even a nonlinear Neural Network (NN) model does not have improved prediction performance compared to the linear predictive model.

There has probably never been such a demand for professionally qualified engineers, and yet both the number and diversity of people entering the profession continue to decline. Worldwide, there are very many initiatives - some generally encouraging interest in the profession, and others targeting specific audiences. The reports speak of local success, but the overall picture remains discouraging. In this paper we focus on the “pipeline” from primary education through to the transition from graduate engineer into an experienced member of engineering staff. We have based the discussion on both the presentations and comments made during a panel discussion held at the 2013 SAE International Congress. The paper is intended as a summary of the points raised during that discussion and, we hope proves to be starting point for further investigation and analysis. Of particular note is the sheer diversity of initiatives, and the pressing need for role models and mentoring.

The idea of grid friendly charging is to use electricity from the grid to charge batteries when electricity is available in surplus and cheap. There are several ways of achieving this, for example using droop control, using night time electricity tariffs, or using smart metering. The goal is twofold: to avoid putting additional load on the electricity grid and power generation, and to reduce the cost to the consumer. This paper looks at the saving potential when charging an electric car using real time tariffs provided by a smart meter, using the Ameren tariffs in Illinois as an example. If prices are known in advance (day-ahead pricing), the optimization only requires picking the cheapest time slots for charging the battery. Further savings can be made by using real time prices that are not known in advance, but the optimization problem then depends on price prediction models, and it becomes much more difficult to solve.

One of the most critical challenges currently facing the diesel engine industry is how to improve fuel economy under emission regulations. Improvement in fuel economy can be achieved by precisely controlling Air/Fuel ratio and by monitoring fuel consumption in real time. Accurate and repeatable measurements of fuel rate play a critical role in successfully controlling air/fuel ratio and in monitoring fuel consumption. Volumetric and gravimetric measurements are well-known methods for measuring fuel consumption of internal combustion engines. However, these methods are not suitable for obtaining fuel flow rate data used in real-time control/measurement. In this paper, neural networks are used to solve the problem concerning discontinuous data of fuel flow rate measured by using an AVL 733 s fuel meter. The continuous parts of discontinuous fuel flow rate are used to train and validate a neural network, which can then be used to predict the discontinuous parts of the fuel flow rate.

More and more stringent emission regulations require advanced control technologies for combustion engines. This goes along with increased monitoring requirements of engine behaviour. In case of emissions behaviour and fuel consumption the actual combustion efficiency is of highest interest. A key parameter of combustion conditions is the in-cylinder pressure during engine cycle. The measurement and detection is difficult and cost intensive. Hence, modelling of in-cylinder conditions is a promising approach for finding optimum control behaviour. However, on-line controller design requires real-time scenarios which are difficult to model and current modelling approaches are either time consuming or inaccurate. This paper presents a new approach of in-cylinder condition prediction. Rather than reconstructing in-cylinder pressure signals from vibration transferred signals through cylinder heads or rods this approach predicts the conditions.

A large contribution to the aerodynamic drag of a vehicle is the loss of pressure in the wake region, especially on square-back configurations. Wake pressure recovery can be achieved by a variety of physical shape changes, but with vehicle shapes becoming ever more aerodynamically efficient research into active technologies for flow manipulation is becoming more prominent. The aim of the current paper is to generate an understanding of how an optimized roof trailing edge, in the form of a chamfer, can reduce wake size, increase base pressures and reduce drag. A comprehensive study using PIV (Particle Image Velocimetry), balance measurements and static pressure measurements was performed in order to investigate the flow and wake structure behind a simplified car model. Significant reductions in C d are demonstrated and directly related to the measured base and slant pressures.

The promise of thermo-electric (TE) technology in vehicles is a low maintenance solid state device for power generation. The Thermo-Electric Generator (TEG) will be located in the exhaust system and will make use of an energy flow between the warmer exhaust gas and the external environment. The potential to make use of an otherwise wasted flow of energy means that the overall system efficiency can be improved substantially. One of the barriers to a successful application of the technology is the device efficiency. The TE properties of even the most advanced materials are still not sufficient for a practical, cost effective device. However the rate of development is such that practical devices are likely to be available within the next fifteen years. In a previous paper [ 1 ], the potential for such a device was shown through an integrated vehicle simulation and TEG model.

Particle Image Velocimetry has developed over the last decade into a relatively mature flow-field measurement technique, capable of providing insight into time averaged and instantaneous flows that in the past have not been readily accessible. The application of the method in the measurement and analysis of flows around road vehicles has so far been limited to a relatively small number of specialist applications, but its use is expanding. This paper reviews the modern digital PIV technique placing emphasis on the important considerations required to obtain reliable and accurate data. This includes comments on each aspect of the PIV process, including initial setup and image acquisition, processing, validation and analysis. A number of automotive case studies are presented covering different aspects of the method, including a diffuser exit flow, edge radius optimization, ‘A’ pillar flow and aerial wake flows.

An experiment is described in which students from two universities, one in the UK and one in the USA, worked together in multidisciplinary teams on aircraft design projects to satisfy the “capstone” design course requirements in their respective degree programs. Aeronautical, Mechanical, Industrial, and Systems Engineering students from Virginia Tech and Loughborough University were placed on teams to work on two different airplane designs. The paper describes the evolution of this educational collaboration and the organization of the experiment. It also reviews the program ’s successes and its problems. Recommendations are made for continuation of the program and to guide others who might be interested in pursuing a similar experiment.

As civil aircraft design matures and the viability of introducing new technological improvements diminishes, new criteria will need to be taken into account in the initial project design phase. This paper considers the overall situation and gives examples of parameters that may become more significant in future aircraft designs. These examples are taken from three different scenarios (aircraft performance, airport compatibility and public safety). The consideration of design aspects which are of a different type to traditional aeronautical engineering parameters will necessitate a change to the processes used in aircraft optimization. Methods developed and used in other disciplines will need to be combined with traditional parameters and the new broader based criteria to provide an overall assessment method. The paper concludes by suggesting a possible line of development.

Coastdown testing is a proven method for determining the drag coefficients for road cars whilst the vehicle is in its normal operating environment. An accurate method of achieving this has been successfully developed at Loughborough University. This paper describes the adaptation and application of these techniques to the special case of a contemporary Formula One racing car. The work was undertaken in conjunction with the Benetton Formula One racing team. The paper outlines the development and application of a suitable mathematical model for this particular type of vehicle. The model includes the aerodynamic, tyre, drivetrain and the un-driven wheel drags and accounts for the change in aerodynamic drag due to ambient wind and changes in vehicle ride height during the coastdown. The test and analysis methods are described.