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In a real-world environment, a vehicle on the road is subjected to a range of flow yaw angles, the most severe of which can impact handling and stability. A fully coupled, six degrees-of-freedom CFD and vehicle handling simulation has modelled the complete closed loop system. Varying flow yaw angles are introduced via time dependent boundary conditions and aerodynamic loads predicted, whilst a handling model running simultaneously calculates the resulting vehicle response. Updates to the vehicle position and orientation within the CFD simulation are achieved using the overset grid method. Using this approach, a crosswind simulation that follows the parameters of ISO 12021:2010 (Sensitivity to lateral wind - Open-loop test method using wind generator input), was performed using the fastback variant of the DrivAer model. Fully coupled aerodynamic and vehicle response was compared to that obtained using the simplified quasi-steady and unsteady, one way coupled method.

A large contribution to the aerodynamic drag of a vehicle (30%(1) or more depending on vehicle shape) arises from the low base pressure in the wake region, especially on square-back configurations. A degree of base pressure recovery can be achieved through careful shape optimization, but the flow structures and mechanisms within the wake that cause these base pressure changes are not well understood. A more complete understanding of these mechanisms may provide opportunities for further drag reductions from both passive shape changes and in the future through the use of active flow control technologies. In this work surprisingly large changes in drag and lift coefficients of a square-back style vehicle have been measured as a result of physically small passive modifications. Tests were performed at quarter scale using a simplified vehicle model (Windsor Model) and at full scale using an MPV. The full scale vehicle was tested with and without a flat floor.

This paper presents a control system design strategy for a novel fuel cell - internal combustion engine hybrid power system. Dynamic control oriented models of the system components are developed. The transient behavior of the system components is investigated in order to determine control parameters and set-points. The analysis presented here is the first step towards development of a controller for this complex system. The results indicate various possibilities for control design and development. A control strategy is discussed to achieve system performance optimization.

This work describes the application of Non-Linear Autoregressive Models with Exogenous Inputs (NLARX) in order to predict the NOx emissions of heavy-duty diesel engines. Two experiments are presented: 1.) a Non-Road-Transient-Cycle (NRTC) 2.) a composition of different engine operation modes and different engine calibrations. Data sets are pre-processed by normalization and re-arranged into training and validation sets. The chosen model is taken from the MATLAB Neural Network Toolbox using the algorithms provided. It is teacher forced trained and then validated. Training results show recognizable performance. However, the validation shows the potential of the chosen method.

In the globalization of the automotive businesses, manufacturing companies and their suppliers are forced to distribute the various lifecycle phases in different geographical locations. Misunderstandings arising from the variety of personnel involved, each with different requirements, backgrounds, roles, cultures and skills for example can result in increased cost and development time. To enable collaborating companies to have a common platform for interaction, the COMPANION project at Loughborough University has been undertaken to develop a common model-based environment for manufacturing automotive engines. Through the use of this environment, the stakeholders will be able to “visualize” consistently the evolution of automated systems at every lifecycle stage i.e. requirements definition, specification, design, analysis, build, evaluation, maintenance, diagnostics and recycle.

This paper reports on an investigation into the potential for a thermoelectric generator (TEG) to improve the fuel economy of a mild hybrid vehicle. A simulation model of a parallel hybrid vehicle equipped with a TEG in the exhaust system is presented. This model is made up by three sub-models: a parallel hybrid vehicle model, an exhaust model and a TEG model. The model is based on a quasi-static approach, which runs a fast and simple estimation of the fuel consumption and CO2 emissions. The model is validated against both experimental and published data. Using this model, the annual fuel saving, CO2 reduction and net present value (NPV) of the TEG’s life time fuel saving are all investigated. The model is also used as a flexible tool for analysis of the sensitivity of vehicle fuel consumption to the TEG design parameters. The analysis results give an effective basis for optimization of the TEG design.

The application of state-of-art thermoelectric generator (TEG) in automotive engine has potential to reduce more than 2% fuel consumption and hence the CO2 emissions. This figure is expected to be increased to 5%~10% in the near future when new thermoelectric material with higher properties is fabricated. However, in order to maximize the TEG output power, there are a few issues need to be considered in the design stage such as the number of modules, the connection of modules, the geometry of the thermoelectric module, the DC-DC converter circuit, the geometry of the heat exchanger especially the hot side heat exchanger etc. These issues can only be investigated via a proper TEG model. The authors introduced four ways of TEG modelling which in the increasing complexity order are MATLB function based model, MATLAB Simscape based Simulink model, GT-power TEG model and CFD STAR-CCM+ model. Both Simscape model and GT-Power model have intrinsic dynamic model performance.

Thermoelectric generator (TEG) has received more and more attention in its application in the harvesting of waste thermal energy in automotive engines. Even though the commercial Bismuth Telluride thermoelectric material only have 5% efficiency and 250°C hot side temperature limit, it is possible to generate peak 1kW electrical energy from a heavy-duty engine. If being equipped with 500W TEG, a passenger car has potential to save more than 2% fuel consumption and hence CO2 emission reduction. TEG has advantages of compact and motionless parts over other thermal harvest technologies such as Organic Rankine Cycle (ORC) and Turbo-Compound (TC). Intense research works are being carried on improving the thermal efficiency of the thermoelectric materials and increasing the hot side temperature limit. Future thermoelectric modules are expected to have 10% to 20% efficiency and over 500°C hot side temperature limit.

The TC48 project is developing a state-of-the-art, exceptionally low cost, 48V Plug-in hybrid electric (PHEV) demonstration drivetrain suitable for electrically powered urban driving, hybrid operation, and internal combustion engine powered high speed motoring. This paper explains the motivation for the project, and presents the layout options considered and the rationale by which these were reduced. The vehicle simulation model used to evaluate the layout options is described and discussed. The modelling work was used in order to support and justify the design choices made. The design of the vehicle's control systems is discussed, presenting simulation results. The physical embodiment of the design is not reported in this paper. The paper describes analysis of small vehicles in the marketplace, including aspects of range and cost, leading to the justification for the specification of the TC48 system.

The goal of grid friendly charging is to avoid putting additional load on the electricity grid when it is heavily loaded already, and to reduce the cost of charging to the consumer. In a smart metering system, Day Ahead tariff (DA) prices are announced in advance for the next day. This information can be used for a simple optimization control, to select to charge at cheapest times. However, the balance of supply and demand is not fully known in advance and the Real-Time Prices (RTP) are therefore likely to be different at times. There is always a risk of a sudden price change, hence adding a stochastic element to the optimization in turn requiring dynamic control to achieve optimal time selection. A stochastic dynamic program (SDP) controller which takes this problem into account has been made and proven by simulation in a previous paper.

Cars in several motor sports series, such as Formula 1, make use of multi-element front wings to provide downforce. These wings also provide onset flows to other surfaces that generate downforce. These elements are highly loaded to maximise their performance and are generally operating close to stall. Rubber debris, often known as marbles, created from the high slip experienced by the soft compound tyres can become lodged in the multiple elements of a front wing. This will lead to a reduction in the effectiveness of the wing over the course of a race. This work will study the effect of such debris, both experimentally and numerically, on an inverted double element wing in ground effect at representative Reynolds numbers. The wing was mounted at two different ride heights above a fixed false-floor in the Loughborough University wind tunnel and the effect of debris blockage modelled by closing sections of the gap between elements with tape.

This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and non-minimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited.

For the vehicles with frequent stop-start operations, fuel consumption can be reduced significantly by implementing stop-start operation. As one way to realize this goal, the pneumatic hybrid technology converts kinetic energy to pneumatic energy by compressing air into air tanks installed on the vehicle. The compressed air can then be reused to drive an air starter to realize a regenerative stop-start function. Furthermore, the pneumatic hybrid can eliminate turbo-lag by injecting compressed air into manifold and a correspondingly larger amount of fuel into the cylinder to build-up full-load torque almost immediately. This paper takes the pneumatic hybrid engine as the research object, focusing on evaluating the improvement of fuel economy of multiple air tanks in different test cycles. Also theoretical analysis the benefits of extra boost on reducing turbo-lag to achieve better performance.

It has been recognised that the ideal flow conditions that exist in the modern automotive wind tunnel do not accurately simulate the environment experienced by vehicles on the road. This paper investigates the effect of varying one flow parameter, freestream turbulence, and a single shape parameter, leading edge radius, on aerodynamic drag. The tests were carried out at model scale in the Loughborough University Wind Tunnel, using a very simple 2-box shape, and in the MIRA Full Scale Wind Tunnel using the MIRA squareback Reference Car. Turbulence intensities up to 5% were generated by grids and had a strong effect on transcritical Reynolds number and Reynolds sensitivity at both model scale and full scale. There was a good correlation between the results in both tunnels.

Thermoelectric generator has very quickly become a hot research topic in the last five years because its broad application area and very attractive features such as no moving parts, low maintenance, variety of thermoelectric materials that total together cover a wide temperature range. The biggest disadvantage of the thermoelectric generator is its low conversion efficiency. So that when design and manufacture a thermoelectric generator for exhaust waste heat recovery from an automotive engine, the benefit of fuel consumption from applying a thermoelectric generator would be very sensitive to the weight, the dimensions, the cost and the practical conversion efficiency. Additionally, the exhaust gas conditions vary with the change of engine operating point. This creates a big challenge for the design of the hot side heat exchanger in terms of optimizing the electrical output of the thermoelectric generator during an engine transient cycle.

Sports Utility Vehicles (SUVs) often have blunt rear end geometries for design and practicality, which is not typically aerodynamic. Drag can be reduced with a number of passive and active methods, which are generally prioritised at zero yaw, which is not entirely representative of the “on road” environment. As such, to combine a visually square geometry (at rest) with optimal drag reductions at non-zero yaw, an adaptive system that applies vertical side edge tapers independently is tested statically. A parametric study has been undertaken in Loughborough University’s Large Wind Tunnel with the ¼ scale Windsor Model. The aerodynamic effect of implementing asymmetric side tapering has been assessed for a range of yaw angles (0°, ±2.5°, ±5° and ±10°) on the force and moment coefficients.

An application in the automotive filed for the Genetic Learning Automata Fuzzy Classifier System is presented in this work. As a non-linear model free-based strategy, the major advantages of this approach are its modularity and its extensibility. A controller designed using this method for a quarter-car model is applied to a 6-DOF model giving a reasonable performance. Comparisons with the LQR controller are also carried out.

In this paper the effects of a rough underbody on the rear wake structure of a simplified squareback model (the Windsor model) is investigated using balance measurements, base pressure measurements and two and three component planar PIV. The work forms part of a larger study to develop understanding of the mechanisms that influence overall base pressure and hence the resulting aerodynamic drag. In the work reported in this paper the impact of a rough underbody on the base pressure and wake flow structures is quantified at three different ground clearances. The underbody roughness has been created through the addition of five roughness strips to the underbody of the model and the effects on the wake at ground clearances of 10.3%, 17.3% and 24.2% of the model height are assessed. All work has been carried out in the Loughborough University Large Wind Tunnel with a ¼ scale model giving a blockage ratio of 4.4% for a smooth under-body or 4.5% with the maximum thickness roughness strips.

Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form.

Hybrid technologies enable the reduction of noxious tailpipe emissions and conformance with ever-decreasing allowable homologation limits. The complexity of the hybrid powertrain technology leads to an energy management problem with multiple energy sinks and sources comprising the system resulting in a high-dimensional time dependent problem for which many solutions have been proposed. Methods that rely on accurate predictions of potential vehicle operations are demonstrably more optimal when compared to rule-based methodology [1]. In this paper, a previously proposed energy management strategy based on an offline optimization using dynamic programming is investigated. This is then coupled with an online model predictive control strategy to follow the predetermined optimal battery state of charge trajectory prescribed by the dynamic program.