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The optimization of turbocharging systems for automotive applications has become crucial in order to increase engine performance and meet the requirements for pollutant emissions and fuel consumption reduction. Unfortunately, performing an optimal turbocharging system control is very difficult, mainly due to the fact that the flow through compressor and turbine is highly unsteady, while only steady flow maps are usually provided by the manufacturer. For these reasons, one of the most important quantities to be used onboard for optimal turbocharger system control is the rotational speed fluctuation, since it provides information both on turbocharger operating point and on the energy of the unsteady flow in the intake and exhaust circuits. This work presents a methodology that allows determining the instantaneous turbocharger rotational speed through a proper frequency processing of the signal coming from one accelerometer mounted on the turbocharger compressor.

This work focuses on the development and validation of a data-driven model capable of predicting the maximum in-cylinder pressure during the operation of an internal combustion engine, with the least possible computational effort. The model is based on two parameters, one that represents engine load and another one the combustion phase. Experimental data from four different gasoline engines, two turbocharged Gasoline Direct Injection Spark Ignition, a Naturally Aspirated SI and a Gasoline Compression Ignition engine, was used to calibrate and validate the model. Some of these engines were equipped with technologies such as Low-Pressure Exhaust Gas Recirculation and Water Injection or a compression ignition type of combustion in the case of the GCI engine. A vast amount of engine points were explored in order to cover as much as possible of the operating range when considering automotive applications and thus confirming the broad validity of the model.

Upcoming more stringent emission regulations throughout the world pose a real challenge, especially in regard to Diesel systems for passenger cars, where the need of additional after-treatment has a big impact in terms of additional system costs and available packaging space. Therefore, the need for strategies that allow managing combustion towards lower emissions, that require a precise control of the combustion outputs, is definitely increasing. Acoustic emission of internal combustion engines contains a large amount of information related to engine behavior and working conditions. Mechanical noise and combustion noise are usually the main contributions to the noise produced by an engine. In particular, recent research from the same authors of this paper demonstrated that combustion noise can be used as an indicator of the combustion that is taking place inside the combustion chamber and therefore as a reference for the control strategy.

Automotive engines and control systems are more and more sophisticated due to increasingly restrictive environmental regulations. Particularly in both diesel and SI lean-burn engines NOx emissions are the key pollutants to deal with and sophisticated Engine Management System (EMS) strategies and after-treatment devices have to be applied. In this context, the in-cylinder oxygen mass fraction plays a key-role due its direct influence on the NOx formation mechanism. Real-time estimation of the intake O₂ charge enhances the NOx prediction during engine transients, suitable for both dynamic adjustments of EMS strategies and management of aftertreatment devices. The paper focuses on the development and experimental validation of a real-time estimator of O₂ concentration in the intake manifold of an automotive common-rail diesel engine, equipped with turbocharger and EGR system.

Multi-jet injection strategies open significant opportunities for the combustion management of the modern diesel engine. Splitting up the injection process into 5 steps facilitates the proper design of the combustion phase in order to obtain the desired torque level, whilst attempting a reduction in emissions, particularly in terms of NOx. Complex 3-D models are needed in the design stage, where components such as the injector or combustion chamber shape have to be determined. Alternatively, zero-dimensional approaches are more useful when fast interpretation of experimental data is needed and an optimization of the combustion process should be obtained based on actual data. For example, zero-dimensional models allow a quick choice of optimum control settings for each engine operating condition, avoiding the need to test all the possible combinations of engine control parameters.

The paper presents a non-intrusive, indirect and low-cost methodology for a real time on-board measurement of an automotive turbocharger rotational speed. In the first part of the paper the feasibility to gather information on the turbocharger speed trend is demonstrated by comparing the time-frequency analysis of the acoustic signal with the direct measurement obtained by an optical sensor facing the compressor blades, mounted in the compressor housing of a spark ignited turbocharged engine. In the second part of the paper, a real time algorithm, to be implemented in the engine control unit, is proposed. The algorithm is able to tune on the turbocharger revolution frequency and to follow it in order to extract the desired speed information. The frequency range containing the turbocharger acoustic frequency can be set utilizing a raw estimation of the compressor speed, derived by its characteristic map.

Theoretical models are useful tools in the design of engine control systems, with applications that range from the design of engine layout, the definition of optimised management systems, to hardware-in-the-loop testing (HiL) and to model-based control strategies. To define theoretical models for control-oriented applications, an original library has been built up at the University of Parma for the simulation of the intake and exhaust systems of automotive turbocharged engines. Starting from this library, a Mean Value Model (MVM) of a Diesel engine, with variable-geometry turbocharger (VGT), EGR and throttle valve, has been developed for a small automotive application. In the paper the matching of the engine model with a detailed model of the exhaust system (developed by Magneti Marelli Powertrain) is presented.

Nowadays requirements towards a reduction in fuel consumption and pollutant emissions of Internal Combustion Engines (ICE) keep on pushing manufacturers to improve engines performance through the enhancement of existing subsystems (e.g.: electronic fuel injection, air systems) and the introduction of specific devices (e.g.: exhaust gas recirculation systems, SCR, …). Modern systems require a combined design and application of different after-treatment devices. Mathematical models are useful tools to investigate the complexity of different system layouts, to design and to validate (HIL/SIL testing) control strategies for the after-treatment management. This study presents a mean value model of an exhaust system with SCR; it has been coupled with a common rail diesel engine combustion black box model (Neural Network based). So, dedicated models for exhaust pipes, oxidation catalyst, diesel particulate filter and selective catalytic converter are developed.

The development of more affordable sensors together with the enhancement of computation features in current Engine Management Systems (EMS), makes the in-cylinder pressure sensing a suitable methodology for the on-board engine control and diagnosis. Since the 1960’s the in-cylinder pressure signal was employed to investigate the combustion process of the internal combustion engines for research purposes. Currently, the sensors cost reduction in addition to the need to comply with the strict emissions legislation has promoted a large-scale diffusion on production engines equipment. The in-cylinder pressure signal offers the opportunity to estimate with high dynamic response almost all the variables of interest for an effective engine combustion control even in case of non-conventional combustion processes (e.g. PCCI, HCCI, LTC).

The new driving cycles require a greater focus on a wider engine operative area and especially in transient conditions where a proper air path control is a challenging task for emission and drivability. In order to achieve this goal, turbocharger speed measurement can give several benefits during boost pressure transient and for over-speed prevention, allowing the adoption of a smaller turbocharger, that can further reduce turbo-lag, also enabling engine down-speeding. So far, the use of turbocharger speed sensor was considered expensive and rarely affordable in passenger car applications, while it is used on high performance engines with the aim of maximizing engine power and torque, mainly in steady state, eroding the safe-margin for turbocharger reliability. Thanks to the availability of a new cost effective turbocharger speed technology, based on acoustic sensing, turbocharger speed measurement has become affordably also for passengers car application.

The need for strategies that allow managing combustion in an adaptive way has recently widely increased. Especially Diesel engines aimed for clean combustion require a precise control of the combustion outputs. Acoustic emission of internal combustion engines contains a lot of information related to engine behavior and working conditions. Mechanical noise and combustion noise are usually the main contributions to the noise produced by an engine. Combustion noise in particular can be used as an indicator of the combustion that is taking place inside the combustion chamber and therefore as a reference for the control strategy. This work discusses the correlations existing between in cylinder combustion and the acoustic emission radiated by the engine and presents a possible approach to use this signal in the engine management system for control purposes.

In the last decades, worldwide automotive regulations induced the industry to dramatically increase the application of electronics in the control of the engine and of the pollutant emissions reduction systems. Besides the need of engine control, suitable fault diagnosis tools had also to be developed, in order to fulfil OBD-II and E-OBD requirements. At present, one of the problems in the development of Diesel engines is represented by the achievement of an ever more sharp control on the systems used for the pollutant emission reduction. In particular, as far as NOx gas is concerned, EGR systems are mature and widely used, but an ever higher efficiency in terms of emissions abatement, requires to determine as better as possible the actual oxygen content in the charge at the engine intake manifold, also in dynamic conditions, i.e. in transient engine operation.

The paper focuses on the experimental identification and validation of different neural networks for virtual sensing of NOx emissions in combustion compression ignition engines (CI). A comparison of several neural network architectures (NN, TDNN and RNN) has been carried out in order to evaluate precision and generalization in dynamic prediction of NOx formation. Furthermore the model complexity (number and types of inputs, neuron and layer number, etc.) has been considered to allow a future ECU implementation and on line training. Suited training procedures and experimental tests are proposed to improve the models. Several measurements of NOx emissions have been performed through different devices applied to the outlet of a EURO 5 Common Rail diesel engine with EGR. The accuracy of the developed models is assessed by comparing simulated and experimental trajectories for a wide range of operating conditions.

The paper deals with the steady-state optimal tuning of control variables for an automotive turbocharged Diesel engine. The optimization analysis is based on an engine simulation model, composed of a control oriented model of turbocharger integrated with a predictive multi-zone combustion model, which allows accounting for the impact of control variables on engine performance, NOx and soot emissions and turbine outlet temperature. This latter strongly affects conversion efficiency of after treatment devices therefore its estimation is of great interest for both control and simulation of tailpipe emissions. The proposed modeling structure is aimed to support the engine control design for common-rail turbocharged Diesel engines with multiple injections, where the large number of control parameters requires a large experimental tuning effort.

Modern internal combustion engine control systems require on-board evaluation of a large number of quantities, in order to perform an efficient combustion control. The importance of optimal combustion control is mainly related to the requests for pollutant emissions reduction, but it is also crucial for noise, vibrations and harshness reduction. Engine system aging can cause significant differences between each cylinder combustion process and, consequently, an increase in vibrations and pollutant emissions. Another aspect worth mentioning is that newly developed low temperature combustion strategies (such as HCCI combustion) deliver the advantage of low engine-out NOx emissions, however, they show a high cylinder-to-cylinder variation. For these reasons, non uniformity in torque produced by the cylinders in an internal combustion engine is a very important parameter to be evaluated on board.

Future regulations on pollutant emissions will impose a drastic cut on Diesel engines out-emissions. For this reason, the development of closed-loop combustion control algorithms has become a key factor in modern Diesel engine management systems. Diesel engines out-emissions can be reduced through a highly premixed combustion portion in low and medium load operating conditions. Since low-temperature premixed combustions are very sensitive to in-cylinder thermal conditions, the first aspect to be considered in newly developed Diesel engine control strategies is the control of the center of combustion. In order to achieve the target center of combustion, conventional combustion control algorithms correct the measured value varying main injection timing. A further reduction in engine-out emissions can be obtained applying an appropriate injection strategy.

The increase in the fuel price and more stringent regulations on greenhouse gases (CO2) make the engine compression ignition technology even more attractive in the context of internal combustion engines. This is because the modern turbocharged direct injection engines, with the common rail fuel system, are characterized by high combustion efficiency and power density, that make them particularly suitable both for applications on and off road. On the other hand, the compression ignition engines are subject to a heavy technological developments to meet the more stringent regulations on emissions of exhaust pollutants, especially PM and NOx. The adopted technologies have two main approaches, on the combustion and on the exhaust gas aftertreatment. The measures applied for combustion can reduce emissions, but with the risk of penalizing the other engine performances, such as noise, power output and fuel consumption.

The paper describes suited methodologies for developing Recurrent Neural Networks (RNN) aimed at estimating NOx emissions at the exhaust of automotive Diesel engines. The proposed methodologies particularly aim at meeting the conflicting needs of feasible on-board implementation of advanced virtual sensors, such as neural network, and satisfactory prediction accuracy. Suited identification procedures and experimental tests were developed to improve RNN precision and generalization in predicting engine NOx emissions during transient operation. NOx measurements were accomplished by a fast response analyzer on a production automotive Diesel engine at the test bench. Proper post-processing of available experiments was performed to provide the identification procedure with the most exhaustive information content. The comparison between experimental results and predicted NOx values on several engine transients, exhibits high level of accuracy.

While the Diesel Particulate Filter (DPF) is actually a quasi-standard equipment in the European Diesel passenger cars market, an interesting solution to fulfill NOx emission limits for the next EU 6 legislation is the application of a Selective Catalytic Reduction (SCR) system on the exhaust line, to drastically reduce NOx emissions. In this context, one of the main issues is the performance of the SCR system during cold start and warm up phases of the engine. The exhaust temperature is too low to allow thermal activation of the reactor and, consequently, to promote high conversion efficiency and significant NOx concentration reduction. This is increasingly evident the smaller the engine displacement, because of its lower exhaust system temperature (reduced gross power while producing the same net power, i.e., higher efficiency).

Over the past years, policies affecting pollutant emissions control for Diesel engines have become more and more restrictive. In order to meet such requirements, innovative combustion control methods have currently become a key factor. Several studies demonstrate that the desired pollutant emission reduction can be achieved through a closed-loop combustion control based on in-cylinder pressure processing. Nevertheless, despite the fact that cylinder pressure sensors for on-board application have been recently developed, large scale deployment of such systems is currently hindered by unsatisfactory long term reliability and high costs. Whereas both the accuracy and the reliability of pressure measurement could be improved in future years, pressure sensors would still be a considerable part of the cost of the entire engine management system.