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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.

Demonstrating the cost/benefits of technologies in the automotive sector is becoming very challenging because the benefits from technologies are sometimes of similar magnitude to testing precision. This paper aims to understand vehicle-borne imprecision and the effect of this on the quality of chassis dynamometer (CD) testing. Fuel consumption and NOx emissions precision is analyzed for two diesel vehicles with particulate filter and SCR systems. The two vehicles were tested on a high precision CD facility over the NEDC (New European Drive Cycle) and WLTC (World harmonized Light-duty Test Cycle) cycles. The CD base precision of testing was characterized between 0.6-3% depending on the cycle phase. A novel application of multi-variate statistical analysis was used to identify the factors that affected testing precision, allowing isolation of small differences that were not obvious when conducting cycle-averaged or cycle-phase-averaged analysis.

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.

Reducing emissions from light duty vehicles is critical to meet current and future air quality targets. With more focus on real world emissions from light-duty vehicles, the interactions between engine and exhaust gas aftertreatment are critical. For modern engines, most emissions are generated during the warm-up phase following a cold start. For Diesel engines this is exaggerated due to colder exhaust temperatures and larger aftertreatment systems. The De-NOx aftertreatment can be particularly problematic. Engine manufacturers are required to take measures to address these temperature issues which often result in higher fuel consumption (retarding combustion, increasing engine load or reducing the Diesel air-fuel ratio). In this paper we consider an inner-insulated turbocharger as an alternative, passive technology which aims to reduce the exhaust heat losses between the engine and the aftertreatment. Firstly, the concept and design of the inner-insulated turbocharger is presented.

Global automotive fuel economy and emissions pressures mean that 48 V hybridisation will become a significant presence in the passenger car market. The complexity of powertrain solutions is increasing in order to further improve fuel economy for hybrid vehicles and maintain robust emissions performance. However, this results in complex interactions between technologies which are difficult to identify through traditional development approaches, resulting in sub-optimal solutions for either vehicle attributes or cost. The results presented in this paper are from a simulation programme focussed on the optimisation of various advanced powertrain technologies on 48 V hybrid vehicle platforms. The technologies assessed include an electrically heated catalyst, an insulated turbocharger, an electric water pump and a thermal management module.

Current turbocharger models are based on characteristic maps derived from experimental measurements taken under steady conditions on dedicated gas stand facility. Under these conditions heat transfer is ignored and consequently the predictive performances of the models are compromised, particularly under the part load and dynamic operating conditions that are representative of real powertrain operations. This paper proposes to apply a dynamic mathematical model that uses a polynomial structure, the Volterra Series, for the modelling of the turbocharger system. The model is calculated directly from measured performance data using an extended least squares regression. In this way, both compressor and turbine are modelled together based on data from dynamic experiments rather than steady flow data from a gas stand. The modelling approach has been applied to dynamic data taken from a physics based model, acting as a virtual test cell.