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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.

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.