Search Results

The European Commission is going to publish the new Euro7 standard shortly, with the target of reducing the impact on pollutant emissions due to transportation systems. Besides forcing internal combustion engines to operate cleaner in a wider range of operating conditions, the incoming regulation will point out the role of On-Board Monitoring (OBM) as a key enabler to ensure limited emissions over the whole vehicle lifetime, necessarily taking into account the natural aging of involved systems and possible electronic/mechanical faults and malfunctions. In this scenario, this work aims to study the potential of data-driven approaches in detecting emission-relevant engine faults, supporting standard On-Board Diagnostics (OBD) in pinpointing faulty components, which is part of the main challenges introduced by Euro7 OBM requirements.

In the field of passenger car engines, recent research advances have proven the effectiveness of downsized, turbocharged and direct injection concepts, applied to gasoline combustion systems, to reduce the overall fuel consumption while respecting particularly stringent exhaust emissions limits. Knock and turbocharger control are two of the most critical factors that influence the achievement of maximum efficiency and satisfactory drivability, for this new generation of engines. The sound emitted from an engine encloses many information related to its operating condition. In particular, the turbocharger whistle and the knock clink are unmistakable sounds. This paper presents the development of real-time control functions, based on direct measurement of the engine acoustic emission, captured by an innovative and low cost acoustic sensor, implemented on a platform suitable for on-board application.

Knock control systems based on engine block vibrations analysis are widely adopted in passenger car engines, but such approach shows its main limits at high engine speeds, since knock intensity measurement becomes less reliable due to the increased background mechanical noise. For small two wheelers engines, knock has not been historically considered a crucial issue, mainly due to small-sized combustion chambers and mixture enrichment. Due to more stringent emission regulations and in search of reduced CO2 emissions, an effective on-board knock controller acquires today greater importance also for motorcycle applications, since it could protect the engine when different fuel types are used, and it could significantly reduce fuel consumption (by avoiding lambda enrichment and/or allowing higher compression ratios to be adopted). These types of engines typically work at high rotational speeds and the reduced signal to noise ratio makes knock onset difficult to identify.

The next steps of the current European and US legislation, EURO 6c and LEV III, and the incoming new test cycles will impose more severe restrictions on pollutant emissions for Gasoline Direct Injection (GDI) engines. In particular, soot emission limits will represent a challenge for the development of this kind of engine concept, if injection and after-treatment systems costs are to be minimized at the same time. The paper illustrates the results obtained by means of a numerical and experimental approach, in terms of soot emissions and combustion stability assessment and control, especially during catalyst-heating conditions, where the main soot quantity in the test cycle is produced. A number of injector configurations has been designed by means of a CAD geometrical analysis, considering the main effects of the spray target on wall impingement.

Combustion phasing is crucial to achieve high performance and efficiency: for gasoline engines control variables such as Spark Advance (SA), Air-to-Fuel Ratio (AFR), Variable Valve Timing (VVT), Exhaust Gas Recirculation (EGR), Tumble Flaps (TF) can influence the way heat is released. The optimal control setting can be chosen taking into account performance indicators, such as Indicated Mean Effective Pressure (IMEP), Brake Specific Fuel Consumption (BSFC), pollutant emissions, or other indexes inherent to reliability issues, such as exhaust gas temperature, or knock intensity. Given the high number of actuations, the calibration of control parameters is becoming challenging.

Measurement of particle number was introduced in the Euro 5/6 light duty vehicle emissions regulation. Due to the complex nature of combustion exhaust particles, and to transportation, transformation and deposition mechanisms, such type of measurement is particularly complex, and regression analysis is commonly used for the comparison of different measurement systems. This paper compares various commercial instruments, developing a correlation analysis focused on PN (Particle Number) measurement, and isolating the factors that mainly influence each measuring method. In particular, the experimental activity has been conducted to allow critical comparisons between measurement techniques that are imposed by current regulations and instruments that can be used also on the test cell. The paper presents the main results obtained by analyzing instruments based on different physical principles, and the effects of different sampling locations and different operating parameters.

The recent advance in the development of various hybrid vehicle technologies comes along with the need of establishing optimal energy management strategies, in order to minimize both fuel economy and pollutant emissions, while taking into account an increasing number of state and control variables, depending on the adopted hybrid architecture. One of the objectives of this research was to establish benchmarking performance, in terms of fuel economy, for real time on-board management strategies, such as ECMS (Equivalent Consumption Minimization Strategy), whose structure has been implemented in a SIMULINK model for different hybrid vehicle concepts.

This paper presents an optimization algorithm, based on discrete dynamic programming, that aims to find the optimal control inputs both for energy and thermal management control strategies of a Plug-in Hybrid Electric Vehicle, in order to minimize the energy consumption over a given driving mission. The chosen vehicle has a complex P1-P4 architecture, with two electrical machines on the front axle and an additional one directly coupled with the engine, on the rear axle. In the first section, the algorithm structure is presented, including the cost-function definition, the disturbances, the state variables and the control variables chosen for the optimal control problem formulation. The second section reports the simplified quasi-static analytical model of the powertrain, which has been used for backward optimization. For this purpose, only the vehicle longitudinal dynamics have been considered.

To meet the requirements in relation to pollutants, CO2-emissions, performances, comfort and costs for 2025 timeframe, many technology options for the powertrain, that plays a key role in the vehicle, are possible. Beside the central aspect of reducing standard cycle consumption levels and emissions, consumer demands are also growing with respect to comfort and functionality. In addition, there is also the challenge of finding cost efficient ways of integrating technologies into a broad range of vehicles with different levels of hybridization. High degrees of electrification simultaneously provide opportunities to reduce the technology content of the internal combustion engines (ICE), resulting in a cost balancing compromise between combustion engine and hybrid technology. The design and optimization of powertrain topologies, functionalities, and components require a complex development process.

Modern turbo-charged downsized engines reach high values of specific power, causing a significant increase of the exhaust gas temperature. Such parameter plays a key role in the overall powertrain environmental impact because it strongly affects both the catalyst efficiency and the turbine durability. In fact, common techniques to properly manage the turbine inlet gas temperature are based on mixture enrichment, which causes both a steep increase in specific fuel consumption and a decrease of catalyst efficiency. At the test bench, exhaust gas temperature is typically measured using thermocouples that are not available for on-board application, and such information is processed to calibrate open-loop look-up-tables. A real-time, reliable, and accurate exhaust temperature model would then represent a strategic tool for improving the performance of the engine control system.

A control oriented model of a Dual Clutch Transmission was developed for real time Hardware In the Loop (HIL) applications. The model is an innovative attempt to reproduce the fast dynamics of the actuation system maintaining a step size large enough for real time applications. The model comprehends a detailed physical description of hydraulic circuit, clutches, synchronizers and gears, and simplified vehicle and internal combustion engine sub-models; a stable real time simulation is achieved with a simplification of the model without losing physical validity. After an offline validation, the model was implemented in a HIL system and connected to the TCU (Transmission Control Unit) via two input-output boards, and to a load plate which comprehends all the actuators.

Turbocharged (TC) engines work at high Indicated Mean Effective Pressure (IMEP), resulting in high in-cylinder pressures and temperatures, improving thermal efficiency, but at the same time increasing the possibility of abnormal combustion events like knock and pre-ignition. To mitigate knocking conditions, engine control systems typically apply spark retard and/or mixture enrichment, which decrease indicated work and increase specific fuel consumption. Many recent studies have advocated Water Injection (WI) as an approach to replace or supplement existing knock mitigation techniques. Water reduces temperatures in the end gas zone due to its high latent heat of vaporization. Furthermore, water vapor acts as diluent in the combustion process. In this paper, the development of a novel closed-loop, model-based WI controller is discussed and critically analyzed.

Due to the ever increasingly stringent emission regulations for passenger vehicles, the efficiency and performance increase of Spark Ignition (SI) engines have been under the focus of the engine manufacturers. The quest for efficiency and performance increase has led to the development of increasingly complex powertrains and control strategies. The development process requires novel methods that feature a smooth transition between the real and the virtual prototypes. Furthermore, to reduce the development time and cost, developing an engine simulator with a low computational effort and good accuracy, which predicts the engine behavior on the entire operating range, plays a crucial role. This work proposes an Artificial Intelligence-based engine simulator for a Spark Ignition engine. The simulator relies on Neural Networks for the calculation of the main combustion metrics. In the first part of this paper, the data acquired at the engine test cell are analyzed.

Due to the recent anti-pollution policies, the performance increase in Spark Ignition (SI) engines is currently under the focus of automotive manufacturers. This trend drives control systems designers to investigate accurate solutions and build more sophisticated algorithms to increase the efficiency of this kind of engines. The development of a control strategy is composed of several phases and steps, and the first part of such process is typically spent in defining and investigating the logic of the strategy. During this phase it is often useful to have a light engine simulator, which allows to have robust synthetic combustion data with a low calibration and computational effort. In the first part of this paper, a description of the control-oriented ANalytical Engine SIMulator (ANESIM) is carried out.

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.

The paper deals with the reduction of pollutant emissions in urban areas by considering a Zero-Emission Zone (ZEZ) in which hybrid electric vehicles (HEVs) are allowed to be driven without using the internal combustion engine, as several cities have planned to realize in the next decades. Moreover, since vehicle connectivity has spread more and more in the last years, a vehicle-to-network (V2N) communication system has been taken into account to retrieve real-time navigation data from a map service provider and thus reconstructing the so-called electronic horizon, which is a reconstruction of the future conditions of the vehicle on the road ahead. The speed profile and the road slope are used as input for an on-board predictive control strategy of a plug-in HEV (PHEV). In particular, a dedicated algorithm predicts the amount of necessary energy to complete the city event in full-electric mode, giving a state of charge (SoC) target value.

During the development of an Internal Combustion Engine-based powertrain, traditional procedures for control strategies calibration and validation produce huge amount of data, that can be used to develop innovative data-driven applications, such as emission virtual sensing. One of the main criticalities is related to the data quality, that cannot be easily assessed for such a big amount of data. This work focuses on an emission modeling activity, using an enhanced Light Gradient Boosting Regressor and a dedicated data pre-processing pipeline to improve data quality. First thing, a software tool is developed to access a database containing data coming from emissions tests. The tool performs a data cleaning procedure to exclude corrupted data or invalid parts of the test. Moreover, it automatically tunes model hyperparameters, it chooses the best set of features, and it validates the procedure by comparing the estimation and the experimental measurement.

The Selective Catalytic Reduction (SCR) system installed on the exhaust line is currently widely used on Diesel heavy-duty trucks and it is considered a promising technique for light and medium duty trucks, large passenger cars and off-highway vehicles, to fulfill future emission legislation. Some vehicles of these last categories, equipped with SCR, have been already put on the market, not only in the US, where the emission legislation on Diesel vehicles is more restrictive, but also in Europe, demonstrating to be already compliant with the upcoming Euro 6. Moreover, new and more stringent emission regulations and homologation cycles are being proposed all over the world, with a consequent rapidly increasing interest for this technology. As a matter of fact, a physical model of the Diesel Exhaust Fluid (DEF) supply system is very useful, not only during the product development phase, but also for the implementation of the on-board real-time controller.

In order to reduce both polluting emissions and fuel costs, many countries allow mixing ethanol to gasoline either in fixed percentages or in variable percentages. The resulting fuel is labeled E10 or E22, where the number specifies the ethanol percentage. This operation significantly changes way the stoichiometric value, which is the air-to-fuel mass ratio theoretically needed to completely burn the mixture. Ethanol concentration must be correctly estimated by the Engine Management System to optimally control exhaust emissions, fuel economy and engine performance. In fact, correct fuel quality recognition allows estimating the actual stoichiometric value, thus allowing the catalyst system to operate at maximum efficiency in any engine working point. Moreover, also other essential engine control functions should be adapted in real time by taking into account the quality of the fuel that is being used.

This work focuses on the effects of cooled Low Pressure EGR and Water Injection observed by conducting experimental tests consisting mainly of Spark Advance sweeps at different cooled LP-EGR and WI rates. The implications on combustion and main engine performance indexes are then analysed and modelled with a control-oriented approach, showing that combustion duration and phase and exhaust gas temperature are the main affected parameters. Results show that cooled LP-EGR and WI have similar effects, being the associated combustion speed decrease the main cause of exhaust gas temperature reduction. Experimental data is used to identify control-oriented polynomial models able to capture the effects of LP-EGR and WI on both these aspects. The limitations of LP-EGR are also explored, identifying maximum compressor volumetric flow and combustion stability as the main ones.