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Engine downsizing and super/turbocharging is currently the most followed trend in order to reduce CO2 emissions and increase the powertrain efficiency. A key challenge for achieving the desired fuel economy benefits lies in optimizing the design and control of the engine boosting system, which requires the ability to rapidly sort different design options and technologies in simulation, evaluating their impact on engine performance and fuel consumption. This paper presents a scalable modeling approach for the characterization of flow and efficiency maps for automotive turbochargers. Starting from the dimensional analysis theory for turbomachinery and a set of well-known control-oriented models for turbocharged engines simulation, a novel scalable model is proposed to predict the flow and efficiency maps of centrifugal compressors and radial inflow turbines as function of their key design parameters.

The need to reduce fuel consumption and harmful pollutants from engines is an important task for automotive industry. It has led to technological advances in new engine design, such as engine downsizing. Due to the reduction of displacement, engine power output is reduced and thus its overall performance is limited. In order to increase torque and power, engines are typically boosted by turbochargers or superchargers. Meanwhile, the improvement on turbo design makes it possible to operate VGT (variable geometry turbocharger) at harsher exhaust environment for gasoline engines as well (e.g., with much higher exhaust temperature than that of diesel engines). This makes VGT related control problems more challenging and requires attention to protecting corresponding engine hardware during an entire engine life.

In the last few years, the application of downsizing and turbocharging to internal combustion engines has considerably increased due to the proven potential of this technology to increase engine efficiency. Variable geometry turbines have been largely adopted to optimize the exhaust energy recovery over a large operating range. Two-stage turbocharger systems have also been studied as a solution to improve engine low-end torque and efficiency, with the first units currently available on the market. However, the compressor technology is today still based on fixed geometry machines, which are sized to efficiently operate at the maximum air flow and therefore lead to poor efficiency values at low air flow conditions. Furthermore, the surge limits prevents the full capabilities of VGT systems to increase the boosting at low engine speed.

The urea injection is a key function in Urea-SCR NOx reduction system. As the tailpipe NOx emission standard becomes increasingly stringent, it is critical to diagnose the injection faults in order to guarantee the SCR DeNox functionality and performance. Particularly, a blocked injector may cause under-dosing of urea thus reduced DeNox functionality. Monitoring urea injection rate is one of the efficient methods for injection fault diagnosis. However, direct measurement of the urea mass flow is not feasible due to its high cost. This paper presents methods that are promising for detecting and isolating faults in urea injection by processing certain actuator signal and existing sensory measurements, e.g., the injector Pulse Amplitude Modulated (PAM) command and the pressure of the urea delivery line. No additional dedicated sensor is required. Three methods are discussed to detect urea injection system faults.

In order to increase the efficiency of automotive turbochargers at low speed without compromising the performance at maximum boost conditions, variable geometry compressor (VGC) systems, based on either variable inlet guide vanes or variable geometry diffusers, have been recently considered as a future design option for automotive turbochargers. This work presents a modeling, analysis and optimization study for a Diesel engine equipped with a variable geometry compressor that help understand the potentials of such technology and develop control algorithms for the VGC systems,. A cycle-averaged engine system model, validated on experimental data, is used to predict the most important variables characterizing the intake and exhaust systems (i.e., mass flow rates, pressures, temperatures) and engine performance (i.e., torque, BMEP, volumetric efficiency), in steady-state and transient conditions.

Fuel economy improvement and stringent emission regulations worldwide require advanced air charging and combustion technologies, such as low temperature combustion, PCCI or HCCI combustion. Furthermore, NOx aftertreatment systems, like Selective Catalyst Reduction (SCR) or lean NOx trap (LNT), are needed to reduce vehicle tailpipe emissions. The information on engine-out NOx emissions is essential for engine combustion optimization, for engine and aftertreatment system development, especially for those involving combustion optimization, system integration, control strategies, and for on-board diagnosis (OBD). A physical NOx sensor involves additional cost and requires on-board diagnostic algorithms to monitor the performance of the NOx sensor.

Two-stage turbochargers are a recent solution to improve engine performance, reducing the turbo-lag phenomenon and improving the matching. However, the definition of the control system is particularly complex, as the presence of two turbochargers that can be in part operated independently requires effort in terms of analysis and optimization. This work documents a characterization study of two-stage turbocharger systems. The study relies on a mean-value model of a Diesel engine equipped with a two-stage turbocharger, validated on experimental data. The turbocharger is characterized by a VGT actuator and a bypass valve (BPV), both located on the high-pressure turbine. This model structure is representative of a “virtual engine”, which can be effectively utilized for applications related to analysis and control. Using this tool, a complete characterization was conducted considering key operating conditions representative of FTP driving cycle operations.

Two-stage turbochargers are a recent solution to improve engine performance. The large flexibility of these systems, able to operate in different modes, can determine a reduction of the turbo-lag phenomenon and improve the engine tuning. However, the presence of two turbochargers that can be in part operated independently requires effort in terms of analysis and optimization to maximize the benefits of this technology. In addition, the design and calibration of the control system is particularly complex. The transitioning between single stage and two-stage operations poses further control issues. In this scenario a model-based approach could be a convenient and effective solution to investigate optimization, calibration and control issues, provided the developed models retain high accuracy, limited calibration effort and the ability to run in real time.

Exhaust gas recirculation (EGR) is one of the key approaches applied to reduce emissions for an internal combustion engine. Recirculating a desired amount of EGR requires accurately estimating EGR mass flow. This can be calculated either from the gas flow equation of an orifice, or from the difference between charge air mass flow and fresh air mass flow. Both calculations need engine exhaust pressure as an input variable. This paper presents a method to estimate exhaust pressure for a variable geometry turbo charged diesel engine. The method is accurate and simple to fit production ECU application, therefore, saves cost of using a physical sensor.