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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 COMponent Based Paradigm for AGile Automation (COMPAG) provides a component-based solution to engine production-line machine control systems. The traditional PLC system is replaced with a distributed control network containing intelligent nodes comprising locally controlled actuators and sensors. The Process Definition Environment provides support for the specification, configuration, and maintenance of the machine control application and facilitates both the initial design and maintenance stages of the lifecycle by describing the control logic as a set of consistent timing and state transition diagrams commonly used in the initial design stages.

Modern control strategies for internal combustion engines use increasingly complex networks of sensors and actuators to measure different physical parameters. Often indirect measurements and estimation of variables, based off sensor data, are used in the closed loop control of the engine and its subsystems. Thus, sensor fusion techniques and virtual instrumentation have become more significant to the control strategy. With the large volumes of data produced by the increasing number of sensors, the analysis of sensor networks has become more important. Understanding the value of the information they contain and how well it is extracted through uncertainty quantification will also become essential to the development of control architecture. This paper proposes a methodology to quantify how valuable a sensor is relative to the architecture. By modelling the sensor network as a Bayesian network, Bayesian analysis and control metrics were used to assess the value of the sensor.

Piston compression rings are thin, incomplete circular structures which are subject to complex motions during a typical 4-stroke internal combustion engine cycle. Ring dynamics comprises its inertial motion relative to the piston, within the confine of its seating groove. There are also elastodynamic modes, such as the ring in-plane motions. A number of modes can be excited, dependent on the net applied force. The latter includes the ring tension and cylinder pressure loading, both of which act outwards on the ring and conform it to the cylinder bore. There is also the radial inward force as the result of ring-bore conjunctional pressure (i.e. contact force). Under transient conditions, the inward and outward forces do not equilibrate, resulting in the small inertial radial motion of the ring.

Model-based calibration (MBC) is a systematic method to calibrate an engine control unit (ECU) system. Due to the working principle of MBC, it is only being used for steady-state systems (time independent models). This limits the use of MBC; because an ECU contains statistical and dynamical systems. Due to the limitations of MBC, dynamical systems require manual tuning which may be time-consuming. With the increasing popularity in hybrid and electrical vehicle systems, most of them rely on dynamical systems. Therefore, MBC is about to be superseded by manual parameterization methods. Remarkably, MBC is not limited to the steady state systems. It can be achieved by separating the time factor of a system and extracting the statistical data from a time series measurement. Typically, MBC model is conceived as the representation of a system plant (i.e.: air path, fuel path, mean value engine model). As a matter of fact, MBC model is not limited to identification of system plant.

Considerations of surface contamination and airborne spray are becoming increasingly significant throughout the automotive design process. Advanced driver assistance systems, such as autonomous cruise control, are growing in popularity. These systems rely on external sensors, the performance of which may be impaired by both direct obstruction and spray. Existing experimental methods of assessing front-end surface contamination and wiper performance have typically utilised fixed spray-grids positioned upstream of the vehicle. The resulting spray is largely steady in nature, in contrast to the unsteady flow-field and tyre spray that would be produced by preceding vehicles. This paper presents the numerical analysis of the spray ejected downstream of a simplified automotive body. The continuous phase (air) is solved using a DDES-based approach coupled with a Lagrangian representation of the dispersed phase (water).

The motivation for this paper is to predict the flow of water over exterior surfaces of road vehicles. We present simulations of liquid flows on solid surfaces under the influence of gravity with and without the addition of aerodynamic forces on the liquid. This is done using an implementation of a Coupled Level Set Volume of Fluid method (CLSVOF) multiphase approach implemented in the open source OpenFOAM CFD code. This is a high fidelity interface-resolving method that solves for the velocity field in both phases without restrictions on the flow regime. In the current paper the suitability of the approach to Exterior Water Management (EWM) is demonstrated using the representative test cases of a continuous liquid rivulet flowing along an inclined surface with a channel located downstream perpendicular to the oncoming flow.

Fuel cell hybrid systems have emerged rapidly in efforts to reduce emissions. The success of these systems mainly depends on implementation of suitable control architectures. This paper presents a control system design for a novel fuel cell - IC Engine hybrid power system. Control oriented models of the system components are developed and integrated. Based on the simulation results of the system model, the control variables are identified. The main objective for the control design is to manage fuel, air and exhaust flows in a way to deliver the required load on the system within local constraints. The controller developed for regulating flows in the system is based on model predictive control theory. The performance of the overall control system is assessed through simulations on a nonlinear dynamic model.

A qualitative FMEA study of Polymer Electrolyte Fuel Cell (PEFC) technology is established and presented in the current work through a literature survey of mechanisms that govern performance degradation and failure. The literature findings are translated into Fault Tree (FT) diagrams that depict how basic events can develop into performance degradation or failure in the context of the following top events; (1) activation losses; (2) mass transportation losses; (3) Ohmic losses; (4) efficiency losses and (5) catastrophic cell failure. Twenty-two identified faults and forty-seven frequent causes are translated into fifty-two basic events and a system of FTs with twenty-one reoccurring dominant mechanisms. The four most dominant mechanisms discussed that currently curtail sustained fuel cell performance relate to membrane durability, liquid water formation, flow-field design, and manufacturing practices.

Full hybrid electric vehicles are usually defined by their capability to drive in a fully electric mode, offering the advantage that they do not produce any emissions at the point of use. This is particularly important in built up areas, where localized emissions in the form of NOx and particulate matter may worsen health issues such as respiratory disease. However, high degrees of electrification also mean that waste heat from the internal combustion engine is often not available for heating the cabin and for maintaining the temperature of the powertrain and emissions control system. If not managed properly, this can result in increased fuel consumption, exhaust emissions, and reduced electric-only range at moderately high or low ambient temperatures negating many of the benefits of the electrification. This paper describes the development of a holistic, modular vehicle model designed for development of an integrated thermal energy management strategy.

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.

Ceramic matrix composites (CMC) have been increasingly used as alternative materials of the rotors of friction brakes. However there is still a need for a better understanding of fundamentals of CMC rotors and their associated friction materials. In this paper, the friction performance at the initial stage was characterized by testing on a laboratory-scale dynamometer and a car for brakes consisting of rotors made of carbon-fiber-reinforced carbon-silicon carbide (Cf/C-SiC) composite, and pads with organic liners. The characteristics of friction surface and its evolution were studied through focused imaging on the surface of the rotor after testing on the dynamometer. Both dynamometer and vehicle tests showed that bedding was essential to reach the required coefficient of friction (CoF). Sustainable transfer layer was successfully deposited on the surface of silicon in the early stage of bedding, but the deposition became difficult on that of carbon constituents and silicon carbide.

GTDI engines are becoming more efficient, whether individually or part of a HEV (Hybrid Electric Vehicle) powertrain. For the latter, this efficiency manifests itself as increase in zero emissions vehicle mileage. An ideal device for energy recovery is a turbogenerator (TG), and, when placed downstream the conventional turbine, it has minimal impact on catalyst light-off and can be used as a bolt-on aftermarket device. A Ricardo WAVE model of a representative GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated using steady state mapping data. This was integrated into a co-simulation environment with a SISO (Single-Input, Single-Output) dynamic controller developed in SIMULINK for the actuator control (with BMEP, manifold air pressure and TG pressure ratio as the controlled variables).

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 paper presents the findings from a numerical study of a gasoline direct injection engine flow using the Large Eddy Simulation (LES) modelling technique. The study is carried out over 30 successive engine cycles. The study illustrates how the more simple but robust Smagorinsky LES sub-grid scale turbulence model can be applied to a complex engine geometry with realistic engineering mesh size and computational expense whilst still meeting the filter width requirements to resolve the majority of large scale turbulent structures. Detailed description is provided here for the computational setup, including the initialisation strategy. The mesh is evaluated using a turbulence resolution parameter and shows the solution to generally resolve upwards of 80% of the turbulence kinetic energy.

A novel semi-analytical solution has been developed for the calculation of the static and dynamic response of an off road tire interacting with a deformable terrain, which utilizes soil parameters independent of the size of the contact patch (size-independent). The models involved in the solution presented, can be categorized in rigid and/or pneumatic tires, with or without tread pattern. After a concise literature review of related methods, a detailed presentation of the semi-analytical solution is presented, along with assumptions and limitations. A flowchart is provided, showing the main steps of the numerical implementation, and various test cases have been examined, characterized in terms of vertical load, tire dimensions, soil properties, deformability of the tire, and tread pattern. It has been found that the proposed model can qualitatively capture the response of a rolling wheel on deformable terrain.

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.

Hybrid electric vehicles offer significant fuel economy benefits, because battery and fuel can be used as complementing energy sources. This paper presents the use of dynamic programming to find the optimal blend of power sources, leading to the lowest fuel consumption and the lowest level of harmful emissions. It is found that the optimal engine behavior differs substantially to an on-line adaptive control system previously designed for the Lotus Evora 414E. When analyzing the trade-off between emission and fuel consumption, CO and HC emissions show a traditional Pareto curve, whereas NOx emissions show a near linear relationship with a high penalty. These global optimization results are not directly applicable for online control, but they can guide the design of a more efficient hybrid control system.

The work presented investigates the effect of road gradient, head-wind, horizontal road curvature, changes in tyre rolling radius, vehicle drag co-efficient and vehicle weight on real-world emission levels of a modern EURO-IV vehicle. A validated steady-state engine performance map based vehicle modeling approach has been used for the analysis. The results showed that a generalized correction factor to include the effect of road-gradient on real-world emission levels might not yield accurate results, since the emission levels are strongly dependent on the position of the vehicle operating parameters on the engine maps. In addition, it also demonstrated that the inclusion of horizontal road curvature such as roundabouts and traffic islands are essential for the estimation of the real-world emission levels.

Sensor selection for the control of modern powertrains is a recognised technical challenge. The key question is which set of sensors is best suited for an effective control strategy? This paper addresses the question through probabilistic modelling and Bayesian analysis. By quantifying uncertainties in the model, the propagation of sensor information throughout the model can be observed. The specific example is an abstract model of the slip behaviour of Selective Catalytic Reduction (SCR) DeNOx aftertreatment systems. Due to the ambiguity of the sensor reading, linearization-based approaches including the Extended Kalman Filter, or the Unscented Kalman Filter are not successful in resolving this problem. The stochastic literature suggests approximating these nonlinear distributions using methods such as Markov Chain Monte Carlo (MCMC), which is able in principle to resolve bimodal or multimodal results.