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This paper investigates the potential for reduced NOx emissions from the integration of thermal factors into the Diesel engine calibration process. NOx emissions from Diesel engines have been shown to be sensitive to engine operating temperature, which is directly related to the level of cooling applied to the engine, in addition to the main engine operating parameters such as injection timing and EGR ratio. Experimental engine characterization of the main engine parameters against coolant temperature set point shows that engine cooling settings can extend the feasible lower limits of fuel consumption and emissions output from Diesel engine. With the adoption of an integrated calibration methodology including engine cooling set point, NOx emissions can be improved by up to 30% at crucial high speed/load operating points seen in the NEDC drive cycle with a minor reduction in fuel economy and small increase in CO output.

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.

The measurement of vehicle modal emissions is technically challenging due to the major issue of determining exhaust gas mass flow rate and ensuring that it is synchronous with the emission measurement of that corresponding ‘slug’ of exhaust gas. This is very evident when attempting to measure small passive NOx catalyst conversion efficiencies. This paper highlights alignment issues with regard to the variation of time delays associated with engine and vehicle events and the CO2 tracer method for determining exhaust gas flows.

Time-alignment sensitivity studies have been carried out to assess the accuracy of instantaneous mass NOx emissions on a chassis rolls dynamometer. The work is part of a larger project aimed at measuring passive NOx catalyst conversion efficiencies. Instantaneous NOx emissions are examined in relation to the NEDC vehicle speed trace at multi sampling points, and phase and time alignment issues are highlighted and discussed. It has been found that a small mismatch of the vehicle speed trace to the instantaneous mass of emissions of ± 2 seconds can lead to results indicating that the conversion efficiency is anywhere between 0-20%. Finally, examples are presented showing the difficulties of attempting to adjust the time alignment of raw emissions data.

Range Extended Electric Vehicles (REEVs) are gaining popularity due to their simplicity, reduced emissions and fuel consumption when compared to parallel or series/parallel hybrid vehicles. The range extender internal combustion engine (ICE) can be optimised to a number of steady state points which offers significant improvement in overall exhaust emissions. One of the key challenges in such vehicles is to reduce the overall powertrain costs, and OEMs providing REEVs such as the BMW i3 have included the range extender as an optional extra due to increasing costs on the overall vehicle price. This paper discusses the development of a low cost Auxiliary Power Unit (APU) of c.25 kW for a range extender application utilising a 624 cc two cylinder automotive gasoline engine. Changes to the base engine are limited to those required for range extender development purposes and include prototype control system, electronic throttle, redesigned manifolds and calibration on European grade fuel.

With ever stricter legislative requirements for CO2 and other exhaust emissions, significant efforts by OEMs have launched a number of different technological strategies to meet these challenges such as Battery Electric Vehicles (BEVs). However, a multiple technology approach is needed to deliver a broad portfolio of products as battery costs and supply constraints are considerable concerns hindering mass uptake of BEVs. Therefore, further investment in Internal Combustion (IC) engine technologies to meet these targets are being considered, such as lean burn gasoline technologies alongside other high efficiency concepts such as dedicated hybrid engines. Hence, it becomes of sound reason to further embrace diversity and develop complementary technologies to assist in the transition to the next generation hybrid powertrain. One such approach is to provide increased valvetrain flexibility to afford new degrees of freedom in engine operating strategies.

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.