Performance assessment of vehicular connectivity in hybrid electric vehicles for fuel and emissions reduction

Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of global warming. Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. In order to address these issues, the present thesis presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. However, even if hybrid electric vehicles have been recognized as a valid solution in response to increasingly tight regulations, the reduced engine load and the repeated engine starts and stops may reduce substantially the temperature of the exhaust after-treatment system (EATS), leading to relevant issues related to pollutant emission control. In this context, electrically heated catalysts (EHCs) represent a promising solution to ensure high pollutant conversion efficiency without affecting engine efficiency and performance. This work aims at studying the advantages provided by the introduction of a predictive EHC control function for a light-duty Diesel plug-in hybrid electric vehicle (PHEV) equipped with a Euro 7-oriented EATS. Based on the knowledge of future driving scenarios provided by vehicular connectivity, engine first start can be predicted and therefore an EATS pre-heating phase can be planned.

Real-Time Engine Diagnostic and Control Based on Acoustic Emissions Analysis

Engine developers are putting more and more emphasis on the research of maximum thermal and mechanical efficiency in the recent years. 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 exhaust emissions limits. These new technologies require more complex engine control units. The sound emitted from a mechanical system encloses many information related to its operating condition and it can be used for control and diagnostic purposes. The thesis shows how the functions carried out from different and specific sensors usually present on-board, can be executed, at the same time, using only one multifunction sensor based on low-cost microphone technology. A theoretical background about sound and signal processing is provided in chapter 1. In modern turbocharged downsized GDI engines, the achievement of maximum thermal efficiency is precluded by the occurrence of knock. Knock emits an unmistakable sound perceived by the human ear like a clink. In chapter 2, the possibility of using this characteristic sound for knock control propose, starting from first experimental assessment tests, to the implementation in a real, production-type engine control unit will be shown. Chapter 3 focus is on misfire detection. Putting emphasis on the low frequency domain of the engine sound spectrum, features related to each combustion cycle of each cylinder can be identified and isolated. An innovative approach to misfire detection, which presents the advantage of not being affected by the road and driveline conditions is introduced. A preliminary study of air path leak detection techniques based on acoustic emissions analysis has been developed, and the first experimental results are shown in chapter 4. Finally, in chapter 5, an innovative detection methodology, based on engine vibration analysis, that can provide useful information about combustion phase is reported.

Modeling and optimization techniques for advanced vehicular networking

This thesis deals with optimization techniques and modeling of vehicular networks. Thanks to the models realized with the integer linear programming (ILP) and the heuristic ones, it was possible to study the performances in 5G networks for the vehicular. Thanks to Software-defined networking (SDN) and Network functions virtualization (NFV) paradigms it was possible to study the performances of different classes of service, such as the Ultra Reliable Low Latency Communications (URLLC) class and enhanced Mobile BroadBand (eMBB) class, and how the functional split can have positive effects on network resource management. Two different protection techniques have been studied: Shared Path Protection (SPP) and Dedicated Path Protection (DPP). Thanks to these different protections, it is possible to achieve different network reliability requirements, according to the needs of the end user. Finally, thanks to a simulator developed in Python, it was possible to study the dynamic allocation of resources in a 5G metro network. Through different provisioning algorithms and different dynamic resource management techniques, useful results have been obtained for understanding the needs in the vehicular networks that will exploit 5G. Finally, two models are shown for reconfiguring backup resources when using shared resource protection.

Modelling, control and testing of internal combustion engines to minimize consumption and pollutant emissions

The objective of the PhD thesis was to research technologies and strategies to reduce fuel consumption and pollutants emission produced by internal combustion engines. In order to meet this objective my activity was focused on the research of advanced controls based on cylinder pressure feedback. These types of control strategies were studied because they present promising results in terms of engine efficiency enhancement. In the PhD dissertation two study cases are presented. The first case is relative to a control strategy to be used at the test bench for the optimisation of the spark advance calibration of motorcycle Engine. The second case is relative to a control strategy to be used directly on board of mining engines with the objective or reducing the engine consumption and correct ageing effects. In both cases the strategies proved to be effective but their implementation required the use of specific toolchains for the measure of the cylinder pressure feedback that for a matter of cost makes feasible the strategy use only for applications: • At test bench • In small-markets like large off-road engines The major bottleneck that prevents the implementation of these strategies on mass production is the cost of cylinder pressure sensor. In order to tackle this issue, during the PhD research, the development of a low-cost sensor for the estimation of cylinder pressure was studied. The prototype was a piezo-electric washer designed to replace the standard spark-plug washer or high-pressure fuel injectors gasket. From the data analysis emerged the possibility to use the piezo-electric prototype signal to evaluate with accuracy several combustion metrics compatible for the implementation of advanced control strategies in on-board applications. Overall, the research shows that advanced combustion controls are feasible and beneficial, not only at the test bench or on stationary engines, but also in mass-produced engines.

Laser cladding and its potential to reduce particulate matter emissions from the automotive and locomotive sector

Laser Cladding (LC) is an emerging technology which is used both for coating applications as well as near-net shape fabrication. Despite its significant advantages, such as low dilution and metallurgical bond with the substrate, it still faces issues such as process control and repeatability, which restricts the extension to its applications. The following thesis evaluates the LC technology and tests its potential to be applied to reduce particulate matter emissions from the automotive and locomotive sector. The evaluation of LC technology was carried out for the deposition of multi-layer and multi-track coatings. 316L stainless steel coatings were deposited to study the minimisation of geometric distortions in thin-walled samples. Laser power, as well as scan strategy, were the main variables to achieve this goal. The use of constant power, reduction at successive layers, a control loop control system, and two different scan strategies were studied. The closed-loop control system was found to be practical only when coupled with the correct scan strategy for the deposition of thin walls. Three overlapped layers of aluminium bronze were deposited onto a structural steel pipe for multitrack coatings. The effect of laser power, scan speed and hatch distance on the final geometry of coating were studied independently, and a combined parameter was established to effectively control each geometrical characteristic (clad width, clad height and percentage of dilution). LC was then applied to coat commercial GCI brake discs with tool steel. The optical micrography showed that even with preheating, the cracks that originated from the substrate towards the coating were still present. The commercial brake discs emitted airborne particles whose concentration and size depended on the test conditions used for simulation in the laboratory. The contact of LC cladded wheel with rail emitted significantly less ultra-fine particles while maintaining the acceptable values of coefficient of friction.

Design and testing of innovative, artificial intelligence-based techniques to model and control the combustion process and to reduce the Co2 emissions in modern, high-performance, gasoline direct injection engines

This work deals with the development of calibration procedures and control systems to improve the performance and efficiency of modern spark ignition turbocharged engines. The algorithms developed are used to optimize and manage the spark advance and the air-to-fuel ratio to control the knock and the exhaust gas temperature at the turbine inlet. The described work falls within the activity that the research group started in the previous years with the industrial partner Ferrari S.p.a. . The first chapter deals with the development of a control-oriented engine simulator based on a neural network approach, with which the main combustion indexes can be simulated. The second chapter deals with the development of a procedure to calibrate offline the spark advance and the air-to-fuel ratio to run the engine under knock-limited conditions and with the maximum admissible exhaust gas temperature at the turbine inlet. This procedure is then converted into a model-based control system and validated with a Software in the Loop approach using the engine simulator developed in the first chapter. Finally, it is implemented in a rapid control prototyping hardware to manage the combustion in steady-state and transient operating conditions at the test bench. The third chapter deals with the study of an innovative and cheap sensor for the in-cylinder pressure measurement, which is a piezoelectric washer that can be installed between the spark plug and the engine head. The signal generated by this kind of sensor is studied, developing a specific algorithm to adjust the value of the knock index in real-time. Finally, with the engine simulator developed in the first chapter, it is demonstrated that the innovative sensor can be coupled with the control system described in the second chapter and that the performance obtained could be the same reachable with the standard in-cylinder pressure sensors.