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With a view to understanding the air-fuel mixing behavior and the effects of the mixture quality on the emissions formation and engine performance, a new quantitative factor of the in-cylinder air-fuel homogeneity named Homogeneity Factor (HF) has been developed. Its characteristics under various injection conditions and air swirl motions within the cylinder have been investigated with CFD simulation. The results have shown that air-fuel homogeneity is essentially affected by the spatial and temporal fuel distribution within the combustion chamber. Higher injection pressure, longer dwell time and increased pilot fuel quantities can contribute to better mixing quality resulting in increased HF and optimum engine performance with low fuel consumption and soot emissions. With regard to the in-cylinder air motion, increasing swirl ratio enhances the air-fuel mixing quality which has been reflected in the variation of the HF.

Diesel engine emissions are directly influenced by the air fuel mixture within the cylinder chamber. Increasing concern over the environment impacts of the exhaust pollutants has enforced the setting of emissions legislation since the 1960s. In the last decades emissions legislations have become stricter which resulted to the introduction of multiple injection strategies and exhaust gas recirculation (EGR) in the cylinder in order to abate emissions produced. In this study, the effect of injection rate for double in-cylinder injection in combination with various EGR and bio-diesel fuel rates has been studied using CFD simulations. Taguchi orthogonal arrays have been used for reducing the number of simulations for possible combinations of different rates of injection quantities, EGR composition and bio-diesel quantities. Oneway analysis of variance technique (ANOVA) has been used to estimate the importance of the above factors to the emissions output and performance of the engine.

New environmental legislation on emission and fuel efficiency targets increasingly requires good transient engine performance and this in turn means that the previously acceptable static engine calibration and control methodologies based on steady-state testing must be re-placed by dynamical optimization using dynamical models. Although many advances have been made in predictive models for internal combustion engines, the phenomena involved are so many, complex and nonlinear that dynamical black-box models typically employing neural network structures must be determined from system identification through experimental testing. Such identified dynamical models are required to provide high accuracy multiple step-ahead predictions of emissions but must accordingly also be compactly implementable for speed and memory to allow for the required large scale optimization involving possibly many thousands of iterations.

During the last years, fuel-cell-based powertrains have been attracting a lot of attention from commercial vehicle manufacturers for reducing vehicle-related Greenhouse Gas (GHG) emissions. Compared to Battery-Electric Vehicles (BEV), fuel-cell-based powertrains has the strong advantage of dealing with range-anxiety, which is crucial for commercial vehicle with high duty-cycle energy requirements. Amongst the different fuel-cell types, Proton Exchange Membrane Fuel-Cells (PEMFC) have the greatest potential for utilization in automotive applications, due to their relatively high technical readiness, market availability and utilization of hydrogen (H2) fuel. In addition, Solid Oxide Fuel-Cells (SOFC) show good potential due to existing re-fueling infrastructure for light hydrocarbon fuels or heavier hydrocarbon fuels (e.g. diesel).

Fuel cell and battery electric powertrains are maturing zero-emission technologies expected to complement each other in the future. At present, battery electric powertrains have emerged competitive for urban light-duty transportation while fuel cell powertrains have emerged competitive in heavy-duty commercial transportation, alongside conventional internal combustion engine propulsion. This paper assesses the benefit for fuel cell powertrains in off-road vehicles, taking into account current and target industry data for powertrain components. Specific emphasis is placed on three important aspects, namely driving range, vehicle weight, and vehicle cost. A model-based design approach is then adopted to size the powertrain to meet a set of performance requirements.