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The paper discusses the concept, design and final results from the ‘Ultra Boost for Economy’ collaborative project, which was part-funded by the Technology Strategy Board, the UK's innovation agency. The project comprised industry- and academia-wide expertise to demonstrate that it is possible to reduce engine capacity by 60% and still achieve the torque curve of a modern, large-capacity naturally-aspirated engine, while encompassing the attributes necessary to employ such a concept in premium vehicles. In addition to achieving the torque curve of the Jaguar Land Rover naturally-aspirated 5.0 litre V8 engine (which included generating 25 bar BMEP at 1000 rpm), the main project target was to show that such a downsized engine could, in itself, provide a major proportion of a route towards a 35% reduction in vehicle tailpipe CO2 on the New European Drive Cycle, together with some vehicle-based modifications and the assumption of stop-start technology being used instead of hybridization.

The paper discusses investigations into improving the full-load and transient performance of the Ultraboost extreme downsizing engine by the application of the SuperGen variable-speed centrifugal supercharger. Since its output stage speed is decoupled from that of the crankshaft, SuperGen is potentially especially attractive in a compound pressure-charging system. Such systems typically comprise a turbocharger, which is used as the main charging device, compounded at lower charge mass flow rates by a supercharger used as a second boosting stage. Because of its variable drive ratio, SuperGen can be blended in and out continuously to provide seamless driveability, as opposed to the alternative of a clutched, single-drive-ratio positive-displacement device. In this respect its operation is very similar to that of an electrically-driven compressor, although it is voltage agnostic and can supply other hybrid functionality, too.

Throttling loss of downsized gasoline engines is significantly smaller than that of naturally aspirated counterparts. However, even the extremely downsized gasoline engine can still suffer a relatively large throttling loss when operating under part load conditions. Various de-throttling concepts have been proposed recently, such as using a FGT or VGT turbine on the intake as a de-throttling mechanism or applying valve throttling to control the charge airflow. Although they all can adjust the mass air flow without a throttle in regular use, an extra component or complicated control strategies have to be adopted. This paper will, for the first time, propose a de-throttling concept in a twin-charged gasoline engine with minimum modification of the existing system. The research engine model which this paper is based on is a 60% downsized 2.0L four cylinder gasoline demonstrator engine with both a supercharger and turbocharger on the intake.

The use of Wankel rotary engines as a range extender has been recognised as an appealing method to enhance the performance of Hybrid Electric Vehicles (HEV). They are effective alternatives to conventional reciprocating piston engines due to their considerable merits such as lightness, compactness, and higher power-to-weight ratio. However, further improvements on Wankel engines in terms of fuel economy and emissions are still needed. The objective of this work is to investigate the engine modelling methodology that is particularly suitable for the theoretical studies on Wankel engine dynamics and new control development. In this paper, control-oriented models are developed for a 225CS Wankel rotary engine produced by Advanced Innovative Engineering (AIE) UK Ltd. Through a synthesis approach that involves State Space (SS) principles and the artificial Neural Networks (NN), the Wankel engine models are derived by leveraging both first-principle knowledge and engine test data.

This paper describes the development and automated calibration of a compact analytically based model of the wall-wetting phenomenon of modern port fuel-injected (PFI) spark-ignition (SI) gasoline engines. The wall-wetting model, based on the physics of forced convection with phase change, is to be used in an automated model-based calibration program. The first stage of work was to develop a model of the wall-wetting phenomenon in Matlab. The model was then calibrated using experimental data collected from a 1.8-litre turbocharged I4 engine coupled to a dynamic 200kW AC dynamometer. The calibration was accomplished by adopting a two stage optimization approach. Firstly, a design of experiments (DoE) approach was used to establish the effect of the principal model parameters on a set of metrics that characterized the magnitude and duration of the measured lambda deviation during a transient.

As emissions regulations become more stringent, an efficient and effective method of rig-based transient engine calibration becomes increasingly desirable. It is known that approximately 80% of total drive-cycle exhaust emissions can be produced in the initial warm-up phase before catalyst ‘light-off’ is achieved and catalyst conversion efficiency increases. During this period, there is a clear trade-off that can be made in the strategy between the amount of thermal energy that is delivered to the catalyst and the amount of exhaust emissions produced during the time before catalyst ‘light-off’ is achieved. This paper examines whether an automated expert-knowledge based decision-making methodology can be used to find a satisfactory trade-off between these two parameters whilst reducing the iteration time and level of input required from a calibration engineer.

Electric coolant pumps for IC engines are under development by a number of suppliers. They offer packaging and flexibility benefits to vehicle manufacturers. Their full potential will not be realised, however, unless an integrated approach is taken to the entire cooling system. The paper describes such a system comprising an advanced electric pump with the necessary flow controls and a supervisory strategy running on an automotive microprocessor. The hardware and control strategy are described together with the simulation developed to allow its calibration and validation before fitting in a B/C class European passenger car. Simulation results are presented which show the system to be controllable and responsive to deliver optimum fuel consumption, emissions and driver comfort.

More stringent legislative guidelines on emissions and fuel economy have led to an increased number of engine parameters to be optimised. Steady state engine mapping to produce an empirical engine model remains a fundamental step in this optimisation process. Recently statistical techniques such as design of experiments have been introduced to improve the efficiency of this modeling phase. Before undertaking an experimental design it is first necessary to determine the permissible envelope of the various design parameters. The importance of this limit space is two fold, firstly to ensure that the engine is not operated in regions, which may cause damage to it, and secondly so that subsequent experimentation yields test data wholly valid for the subsequent engine model. Currently this limit space is defined in a largely manual process, requiring expert input many times in the test and characterization process.

The major contribution of this paper is the general description of a complete integrating procedure of autonomous vehicle system. Using Robot Operating System (ROS) as the framework, process from senor integration to path planning and path tracking were performed. Based on an off-road All-Terrain Vehicle, an Extended Kalman filter based autonomous control strategy was developed on the ROS. Both the position estimation and autonomous control were performed on the ROS platform. For the position estimation phase, sensory measurements from GPS, IMU and wheel odometry were acquired and processed on ROS. In accordance with the ROS architecture, separate packages were developed for each sensor to gather and publish corresponding measurements. Furthermore, Extended Kalman filtering was performed to fuse all sensory measurements to achieve an optimizing accuracy.

The series sequential turbocharging technology is recently gaining attention as the new round of engine downsizing and emission control becomes imperative for the engine manufacturers. The technology is able to provide combined benefits of transient performance, engine downsizing, fuel efficiency and emissions reduction with foreseeable problems of control, packaging and cost. The matching and characterization of the two interactive turbochargers is a challenging exercise. Two important questions are, how should the two machines be sized and what is the best strategy for the turbochargers across the speed range of the engine at full load. This paper addresses these two questions by comparing a variety of matching sizes and presenting an attempt to identify an optimal valve operating schedule in order to achieve the target limiting torque curve.

This paper introduces the work ongoing at the University of Bath in a series of projects aimed at characterising the driveability of CVT equipped vehicles and using the findings to help develop a strategy for a prototype powertrain controller during transient driving situations. Results of the driveability investigation of a first project in this series have already been published [1], where the driveability of three CVT vehicles was appraised. A follow-up project extends this work appraising more CVT vehicles and also comparing driveability aspects of CVT transmissions to conventional AT. The paper relates the common experimental part of the two projects showing linked results and describing how a simulation program can be used to predict and improve the driveability of the powertrain controller.

The Divided Exhaust Period (DEP) concept is an approach which has been proved to significantly reduce the averaged back pressure of turbocharged engines whilst still improving its combustion phasing. The standard layout of the DEP system comprises of two separately-functioned exhaust valves with one valve feeding the blow-down pulse to the turbine whilst the other valve targeting the scavenging behaviour by bypassing the turbine. Via combining the characteristics of both turbocharged engines and naturally aspirated engines, this method can provide large BSFC improvement. The DEP concept has only been applied to single-stage turbocharged engines so far. However, it in its basic form is in no way restricted to a single-stage system. This paper, for the first time, will apply DEP concept to a regulated two-stage (R2S) downsized SI engine.

Nitrogen oxides emissions are an important aspect of engine design and calibration due to increasingly strict legislation. As a consequence, accurate modeling of nitrogen oxides emissions from Diesel engines could play a crucial role during the design and development phases of vehicle powertrain systems. A key step in future engine calibration will be the need to capture the nonlinear behavior of the engine with respect to nitrogen oxides emissions within a rapid-calculating mathematical model. These models will then be used in optimization routines or on-board control features. In this paper, an artificial neural network structure incorporating a number of engine variables as inputs including torque, speed, oil temperature and variables related to fuel injection is developed as a method of predicting the production of nitrogen oxides based on measured test data. A multi-layer perceptron model is identified and validated using data from dynamometry tests.

In passenger car development, extreme ICE downsizing trends have been observed over the past decade. While this comes with fuel economy benefits, they are often obtained at the expense of Brake Mean Effective Pressure (BMEP) rise time in transient engine response. Through advanced control strategies, the use of Fully Variable Valvetrain (FVVT) technologies has the potential to completely mitigate the associated drivability-penalizing constraints. Adopting a statistical approach, key part load performance engine parameters are analyzed. Design-of-Experiment data is generated using a validated GT-Power model for a Freevalve-converted turbocharged Ultraboost engine. Subsequently, MathWorks' Model Based Calibration (MBC) toolbox is utilized to interpret the data through model fitments using neural network models of optimized architectures.