Design, production and testing of innovative key components of HEV/BEV powertrains

Through modelling activity, experimental campaigns, test bench and on-field validation, a complete powertrain for a BEV has been designed, assembled and used in a motorsport competition. The activity can be split in three main subjects, representing the three key components of an BEV vehicle. First of all a model of the entire powertrain has been developed in order to understand how the various design choices will influence the race lap-time. The data obtained was then used to design, build and test a first battery pack. After bench tests and track tests, it was understood that by using all the cell charac-teristics, without breaking the rules limitations, higher energy and power densities could have been achieved. An updated battery pack was then designed, produced and raced with at Motostudent 2018 re-sulting in a third place at debut. The second topic of this PhD was the design of novel inverter topologies. Three inverters have been de-signed, two of them using Gallium Nitride devices, a promising semiconductor technology that can achieve high switching speeds while maintaining low switching losses. High switching frequency is crucial to reduce the DC-Bus capacitor and then increase the power density of 3 phase inverters. The third in-verter uses classic Silicon devices but employs a ZVS (Zero Voltage Switching) topology. Despite the in-creased complexity of both the hardware and the control software, it can offer reduced switching losses by using conventional and established silicon mosfet technology. Finally, the mechanical parts of a three phase permanent magnet motor have been designed with the aim to employ it in UniBo Motorsport’s 2020 Formula Student car.

Development and testing of innovative methodologies for modelling and control of normal and knocking combustion and implementation of novel rapid prototyping solutions

The thesis work deals with topics that led to the development of innovative control-oriented models and control algorithms for modern gasoline engines. Knock in boosted spark ignition engines is the widest topic discussed in this document because it remains one of the most limiting factors for maximizing combustion efficiency in this kind of engine. First chapter is thus focused on knock and a wide literature review is proposed to summarize the preliminary knowledge that even represents the background and the reference for discussed activities. Most relevant results achieved during PhD course in the field of knock modelling and control are then presented, describing every control-oriented model that led to the development of an adaptive model-based combustion control system. The complete controller has been developed in the context of the collaboration with Ferrari GT and it allowed to completely redefine the knock intensity evaluation as well as the combustion phase control. The second chapter is focused on the activity related to a prototyping Port Water Injection system that has been developed and tested on a turbocharged spark ignition engine, within the collaboration with Magneti Marelli. Such system and the effects of injected water on the combustion process were then modeled in a 1-D simulation environment (GT Power). Third chapter shows the development and validation of a control-oriented model for the real-time calculation of exhaust gas temperature that represents another important limitation to the performance increase in modern boosted engines. Indeed, modelling of exhaust gas temperature and thermocouple behavior are themes that play a key role in the optimization of combustion and catalyst efficiency.

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.

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.

Hardware-software design of embedded systems for intelligent sensing applications

This Thesis wants to highlight the importance of ad-hoc designed and developed embedded systems in the implementation of intelligent sensor networks. As evidence four areas of application are presented: Precision Agriculture, Bioengineering, Automotive and Structural Health Monitoring. For each field is reported one, or more, smart device design and developing, in addition to on-board elaborations, experimental validation and in field tests. In particular, it is presented the design and development of a fruit meter. In the bioengineering field, three different projects are reported, detailing the architectures implemented and the validation tests conducted. Two prototype realizations of an inner temperature measurement system in electric motors for an automotive application are then discussed. Lastly, the HW/SW design of a Smart Sensor Network is analyzed: the network features on-board data management and processing, integration in an IoT toolchain, Wireless Sensor Network developments and an AI framework for vibration-based structural assessment.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.

Development and testing of a safety module for diagnostics and protection from hazards in secondary woodworking machinery

The main theme covered by this dissertation is safety, set in the context of automatic machinery for secondary woodworking. The thesis describes in detail the project of a software module for CNC machining centers to protect the operator against hazards and to report errors in the machine safety management. Its design has been developed during an internship at SCM Group technical department. The development of the safety module is addressed step by step in a detailed way: first the company and the reference framework are introduced and then all the design choices are explained and justified. The discussion begins with a detailed analysis of the standards concerning woodworking machines and safety-related software. In this way, a clear and linear procedure can be established to develop and implement the internal structure of the module, its interface, and its application to specific safety-critical conditions. Afterwards, particular attention is paid to software testing, with the development of a comprehensive test procedure for the module, and to diagnostics, especially oriented towards signal management in IoT mode. Finally, the safety module is used as an anti-regression tool to initiate a design improvement of the machine control program. The refactoring steps performed in the process are explained in detail and the SCENT approach is introduced to test the result.

Development and testing of a virtual energy meter using the Labview Environment

The quantity of electric energy utilized by a home, a business, or an electrically powered device is measured by an electricity meter, also known as an electric meter, electrical meter, or energy meter. Electric meters located at customers' locations are used by electric providers for billing. They are usually calibrated in billing units, with the kilowatt hour being the most popular (kWh). Typically, they are read once each billing cycle. When energy savings are sought during specific times, some meters may monitor demand, or the highest amount of electricity used during a specific time. Additionally, some meters feature relays for load shedding in response to responses during periods of peak load. The amount of electrical energy consumed by users is measured by a Watt-hour meter, also known as an energy meter. To charge the electricity usage by loads like lights, fans, and other appliances, utilities put these gadgets everywhere, including in households, businesses, and organizations. Watts are a fundamental power unit. A kilowatt is equal to one thousand watts. One kilowatt is regarded as one unit of energy used if used for one hour. These meters calculate the product of the instantaneous voltage and current readings and provide instantaneous power. This power is distributed over a period and is used during that time. Depending on the supply used by home or commercial installations, these may be single or three phase meters. These can be linked directly between line and load for minor service measurements, such as home consumers. However, step-down current transformers must be installed for greater loads to handle their higher current demands.

Comparison of Creep and Resilient Modulus Laboratory Results with Field Cores, HR-311, Part 3, 1993

This is Part 3 of a study of creep and resilient modulus testing of hot mix asphalt concrete. The creep and resilient modulus testing in Part 1 showed the improved load carrying characteristics of crushed particles. Cores from pavements drilled in Part 2 exhibited a poor correlation with rutting and creep/resilient modulus on pavement with a range of rut depths. The objective of Part 3 was to determine the relationship of creep and resilient modulus for 1) Marshall specimens from laboratory mixing for mix design; 2) Marshall specimens from construction plant mixing; and 3) cores drilled from the hot mixed asphalt pavement. The creep and resilient modulus data from these three sources exhibited substantial variations. No meaningful correlations of the results from these three sources were obtained.

Alkali Content of Fly Ash – Measuring and Testing Strategies for Compliance, TR-628, 2015

Sodium and potassium are the common alkalis present in fly ash. Excessive amounts of fly ash alkalis can cause efflorescence problems in concrete products and raise concern about the effectiveness of the fly ash to mitigate alkali-silica reaction (ASR). The available alkali test, which is commonly used to measure fly ash alkali, takes approximately 35 days for execution and reporting. Hence, in many instances the fly ash has already been incorporated into concrete before the test results are available. This complicates the job of the fly ash marketing agencies and it leads to disputes with fly ash users who often are concerned with accepting projects that contain materials that fail to meet specification limits. The research project consisted of a lab study and a field study. The lab study focused on the available alkali test and how fly ash alkali content impacts common performance tests (mortar-bar expansion tests). Twenty-one fly ash samples were evaluated during the testing. The field study focused on the inspection and testing of selected, well documented pavement sites that contained moderately reactive fine aggregate and high-alkali fly ash. A total of nine pavement sites were evaluated. Two of the sites were control sites that did not contain fly ash. The results of the lab study indicated that the available alkali test is prone to experimental errors that cause poor agreement between testing labs. A strong (linear) relationship was observed between available alkali content and total alkali content of Class C fly ash. This relationship can be used to provide a quicker, more precise method of estimating the available alkali content. The results of the field study failed to link the use of high-alkali fly ash with the occurrence of ASR in the various concrete sites. Petrographic examination of the pavement cores indicated that Wayland sand is an ASR-sensitive aggregate. This was in good agreement with Iowa DOT field service records. It was recommended that preventative measures should be used when this source of sand is used in concrete mixtures.

Relating Creep Testing to Rutting of Asphalt Concrete Mixes, HR-311, Part 2, 1991

The Iowa Department of Transportation began creep and resilient modulus testing of asphalt concrete mixtures in 1989. Part 1 of this research reported in January 1990 was a laboratory study of hot mix asphalt (HMA) mixtures made with O, 30, 60, 85 and 100% crushed gravel, crushed limestone and crushed quartzite combined with uncrushed sand and gravel. Creep test results from Marshall specimens related well to the percent of crushed particles and the perceived resistance to rutting. The objective of this research, part 2, was to determine if there was a meaningful correlation between pavement rut depth and the resilient modulus or the creep resistance factor. Four and six inch diameter cores were drilled from rutted primary and interstate pavements and interstate pavements with design changes intended to resist rutting. The top 2 1/2 inches of each core, most of which was surface course, was used for creep and resilient modulus testing. There is a good correlation between the resilient modulus of four and six inch diameter cores. Creep resistance factors of four and six inch diameter cores also correlated well. There is a poor correlation between resilient modulus and the creep resistance factor. The rut depth per million 18,000 pound equivalent single axle loadings (ESAL) for these pavements did not correlate well with either the resilient modulus or the creep resistance factor.

Development of an instrumentation system embedded on FPGA for real time measurement of mechanical vibrations in rotating machinery

The real-time monitoring of events in an industrial plant is vital, to monitor the actual conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes condition monitoring of the rotating machinery, to anticipate possible conditions of failure. To increase the operational reliability it is thus necessary an efficient tool to analyze and monitor the equipments, in real-time, and enabling the detection of e.g. incipient faults in bearings. To fulfill these requirements some innovations have become frequent, namely the inclusion of vibration sensors or stator current sensors. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades, and replacement of the monitored machine, as well as expansion of the monitoring system. This paper presents the development, implementation and testing of an instrument for vibration monitoring, as a possible solution to embed in industrial environment. The digital control system is based on an FPGA, and its configuration with an open hardware design tool is described. Special focus is given to the area of fault detection in rolling bearings. © 2012 IEEE.

A numerical discrete element study on the influence of an embedded viscoelastic-plastic layer at different viscosity values

The major aim of this study was to examine the influence of an embedded viscoelastic-plastic layer at different viscosity values on accretionary wedges at subduction zones. To quantify the effects of the layer viscosity, we analysed the wedge geometry, accretion mode, thrust systems and mass transport pattern. Therefore, we developed a numerical 2D 'sandbox' model utilising the Discrete Element Method. Starting with a simple pure Mohr Coulomb sequence, we added an embedded viscoelastic-plastic layer within the brittle, undeformed 'sediment' package. This layer followed Burger's rheology, which simulates the creep behaviour of natural rocks, such as evaporites. This layer got thrusted and folded during the subduction process. The testing of different bulk viscosity values, from 1 × 10**13 to 1 × 10**14 (Pa s), revealed a certain range where an active detachment evolved within the viscoelastic-plastic layer that decoupled the over- and the underlying brittle strata. This mid-level detachment caused the evolution of a frontally accreted wedge above it and a long underthrusted and subsequently basally accreted sequence beneath it. Both sequences were characterised by specific mass transport patterns depending on the used viscosity value. With decreasing bulk viscosities, thrust systems above this weak mid-level detachment became increasingly symmetrical and the particle uplift was reduced, as would be expected for a salt controlled forearc in nature. Simultaneously, antiformal stacking was favoured over hinterland dipping in the lower brittle layer and overturning of the uplifted material increased. Hence, we validated that the viscosity of an embedded detachment strongly influences the whole wedge mechanics, both the respective lower slope and the upper slope duplex, shown by e.g. the mass transport pattern.

The Utility of the CVLT-C as an Embedded Validity Indicator After Pediatric Mild Traumatic Brain Injury.

Results of neuropsychological examinations depend on valid data. Whereas clinicians previously believed that clinical skill was sufficient to identify non-credible performance by examinees on standard tests, research demonstrates otherwise. Consequently, studies on measures to detect suspect effort in adults have received tremendous attention in the previous twenty years, and incorporation of validity indicators into neuropsychological examinations is now seen as integral. Few studies exist that validate methods appropriate for the measurement of effort in pediatric populations. Of extant studies, most evaluate standalone measures originally developed for use with adults. The present study examined the utility of indices from the California Verbal Learning Test – Children's Version (CVLT-C) as embedded validity indicators in a pediatric sample. Participants were 225 outpatients aged 8 to 16 years old referred for clinical assessment after mild traumatic brain injury (mTBI). Non-credible performance (n = 39) was defined as failure of the Medical Symptom Validity Test (MSVT). Logistic regression demonstrated that only the Recognition Discriminability index was predictive of MSVT failure (OR = 2.88, p < .001). A cutoff of z ≤ -1.0 was associated with sensitivity of 51% and specificity of 91%. In the current study, CVLT-C Recognition Discriminability was useful in the identification of non-credible performance in a sample of relatively high-functioning pediatric outpatients with mTBI. Thus, this index can be added to the short list of embedded validity indicators appropriate for pediatric neuropsychological assessment.

Average-case analysis of power consumption in embedded systems

Power efficiency is one of the most important constraints in the design of embedded systems since such systems are generally driven by batteries with limited energy budget or restricted power supply. In every embedded system, there are one or more processor cores to run the software and interact with the other hardware components of the system. The power consumption of the processor core(s) has an important impact on the total power dissipated in the system. Hence, the processor power optimization is crucial in satisfying the power consumption constraints, and developing low-power embedded systems. A key aspect of research in processor power optimization and management is “power estimation”. Having a fast and accurate method for processor power estimation at design time helps the designer to explore a large space of design possibilities, to make the optimal choices for developing a power efficient processor. Likewise, understanding the processor power dissipation behaviour of a specific software/application is the key for choosing appropriate algorithms in order to write power efficient software. Simulation-based methods for measuring the processor power achieve very high accuracy, but are available only late in the design process, and are often quite slow. Therefore, the need has arisen for faster, higher-level power prediction methods that allow the system designer to explore many alternatives for developing powerefficient hardware and software. The aim of this thesis is to present fast and high-level power models for the prediction of processor power consumption. Power predictability in this work is achieved in two ways: first, using a design method to develop power predictable circuits; second, analysing the power of the functions in the code which repeat during execution, then building the power model based on average number of repetitions. In the first case, a design method called Asynchronous Charge Sharing Logic (ACSL) is used to implement the Arithmetic Logic Unit (ALU) for the 8051 microcontroller. The ACSL circuits are power predictable due to the independency of their power consumption to the input data. Based on this property, a fast prediction method is presented to estimate the power of ALU by analysing the software program, and extracting the number of ALU-related instructions. This method achieves less than 1% error in power estimation and more than 100 times speedup in comparison to conventional simulation-based methods. In the second case, an average-case processor energy model is developed for the Insertion sort algorithm based on the number of comparisons that take place in the execution of the algorithm. The average number of comparisons is calculated using a high level methodology called MOdular Quantitative Analysis (MOQA). The parameters of the energy model are measured for the LEON3 processor core, but the model is general and can be used for any processor. The model has been validated through the power measurement experiments, and offers high accuracy and orders of magnitude speedup over the simulation-based method.