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Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms now mean that these are becoming an important tool in engine research. In this work a random forest (RF) algorithm is used for the prediction of PN emissions from a highly boosted (up to 32 bar BMEP) GDI engine. Particle size, concentration, and the accumulation mode geometric standard deviation (GSD) are all predicted by the model. The results are analysed and an in depth study on parameter importance is carried out.

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.

The gradual push towards electric vehicles (EV) as a primary mode of transport has resulted in an increased focus on electric and hybrid powertrain research. One answer to the consumers’ concern over EV range is the implementation of small combustion engines as generators to supplement the energy stored in the vehicle battery. Since these range extender generators have the opportunity to run in a small operating window, some engine types that have historically struggled in an automotive setting have the potential to be competitive. The relative merits of two different engine options for range extended electric vehicles are simulated in vehicle across the WLTP drive cycle. The baseline electric vehicle chosen was the BMW i3 owing to its availability as an EV with and without a range extender gasoline engine.

When evaluating the performance of new boosting hardware, it is a challenge to isolate the heat transfer effects inherent within measured turbine and compressor efficiencies. This work documents the construction of a lumped mass turbocharger model in the MatLab Simulink environment capable of predicting turbine and compressor metal and gas outlet temperatures based on measured or simulated inlet conditions. A production turbocharger from a representative 2.2L common rail diesel engine was instrumented to enable accurate gas and wall temperature measurements to be recorded under a variety of engine operating conditions. Initially steady-state testing was undertaken across the engine speed and load range in order that empirical Reynolds-Nusselt heat transfer relationships could be derived and incorporated into the model. Steady state model predictions were validated against further experimental data.

In recent years, there has been an impetus in the automotive industry to develop newer diesel injection systems with a view to reducing fuel consumption and emissions. This development has led to hardware capable of higher pressures, typically up to 2500 bar. An increase in pressure will result in a corresponding increase in fuel temperature after compression with studies showing changes in fuel temperatures of up to 150 °C in 1000-2500 bar injection systems. Until recently, the addition of Fatty Acid Methyl Esters, FAME, to diesel had been blamed for a number of fuel system durability issues such as injector deposits and fuel filter blocking. Despite a growing acceptance within the automotive and petrochemical industries that FAME is not solely to blame for diesel instability, there is a lack of published literature in the area, with many studies still focusing on FAME oxidation to explain deposit formation and hardware durability.