The global push for sustainable development and lower energy usage has prompted increased interest in using automation and control systems in building infrastructures. These systems are now being investigated in Mozambique to determine their ability to increase energy efficiency. This study focuses on the complex processes of creating, deploying, and assessing these systems which use cutting-edge technology such as sensors, machine learning algorithms, and smart devices. The study includes case studies and experimental results that demonstrate the real-world usefulness of these methods. The capacity of automation and control systems to regulate temperature, lighting, and other energy-intensive components significantly reduces overall energy usage within buildings. Furthermore, these systems can discover and diagnose defects in a building's energy systems, allowing for fast and efficient repair—a critical factor in Mozambique, where energy supply is frequently unstable and maintenance is complex. The results of this study imply considerable energy savings, which will help to reduce the country's carbon footprint and advance sustainable development goals. The combination of advanced technologies, practical effectiveness in real-world scenarios, contextual relevance to Mozambique, a holistic approach, emphasis on timely maintenance, and the promotion of sustainable development make this study unique and valuable in building energy efficiency. In conclusion, this study catalyses the creation and implementation of Building Energy Management Systems in Mozambique by providing relevant insights for policymakers, building owners, and energy managers.

In the green motor vehicle era, fuel cell hydrogen electric vehicles (FCHEVs) are becoming promising alternatives. Thus, ensuring the proper operation of FCHEVs solely depends on advanced energy management systems (EMS). In this light, this work looks deeply into how combining machine learning and physics-based models can make FCHEVs operate effectively through improved EMSs. This study extensively analyzes how machine learning and physics-based models operate together in FCHEV-EMS. It therefore breaks through existing research and identifies insights, challenges, and potential future directions. It also looks closely at how machine learning meets challenges in adapting to real-time and handling changing conditions. To gain a better understanding of these issues, this study further recommends innovative ways to integrate machine learning flexibility within the precision of physics-based modeling. It therefore reveals an intriguing potential for additional study in the world of FCHEV-EMS. It represents the integration of machine learning and physics-based models as a potent technique to deal with EMS difficulties and accelerate advances in FCHEV energy management. In the end, it outlines significant findings, addressing why this integrated strategy is crucial in making FCHEVs a world-leading sustainable means of transportation. Through its comprehensive review and strategic perspectives, this initiative aims at catalyzing innovations that actively contribute to the sustainable advancement of FCHEVs.

Driving is one of the prominent necessities of any individual. Due to the growth of the population, transportation has become tight, thus increasing the risk of accidents. There are several reasons that have been highlighted for such accidents, namely, long-distance driving without appropriate relaxation, driving at night, using mobile devices while driving, and driving under the influence of alcohol. Even though several pieces of research are available on automatic braking, this research specifically analyzes the importance of real-time data, while the system is driver-assisted. This research comprised an ultrasonic sensor, speed sensor, Arduino UNO Board, rain sensor, DC actuator, air compressor, and air reservoirs along with the major elements of the braking system. The results were analyzed in three various situations in wet and dry conditions. In addition, 1m of tolerance was provided for the internal adjustments of the vehicle. At first sight, if the distance between the object and the vehicle is larger than the braking distance tolerance limit, the system functions normally where activation is unnecessary. If the distance between the vehicle and the object interface is less than the braking distance tolerance limit, using the sensing mechanism, the brakes will be actuated and the vehicle will stop immediately. Finally, when the vehicle and object are too close, the system will not function again while giving a warning sign for the driver to steer in the proper direction. Overall, the variation in braking distance concerning vehicle speed was analyzed in both wet and dry conditions. Indeed, the research results provided a practically applicable automatic braking system with the driver’s control. This proactive method of accident mitigation represents a novel solution with the future scope of coordinating the automated braking system with a speed reduction system by regulating the accelerator's rotation.

Dynamometers are specifically designed for the measurement of the engine’s brake power. Although several types are physically available, disc brake dynamometers stand out as a more accurate and easily manipulable system. This paper aims to develop a highly accurate disc brake dynamometer while establishing the relationships between several process parameters. In the methodology, the initial stage was to measure the force requirements to the accelerator, brake lever, and clutch. A 3D model was developed using AutoCAD and the necessary accessories were identified. A CG125 engine was selected for the study. The most relevant preliminary design stages were formulated before the experimentation. An interface was added to display the outcome of the analysis. In the results, a real-time graphical relationship was built for brake power and engine speed. Seven sets of data in two different circumstances were obtained. The obtained results were validated against previous experimental results. Both sets of results were matched in most situations for the selected engine. The variation was comparatively less. The engine RPM was stipulated between 2000 and 8000, with the maximum power at the upper limit. The developed domestic application provided major benefits such as the control of the system at a single location, the automatic generation of relationships between the concerned parameters, the presence of a safety switch that can immediately halt the process in emergencies, the use of lambda sensors for corrections, and less maintenance. In terms of limitations, the system is limited to a permanent engine. Thus, this research can be further improved upon with the use of several engines at a time. Errors concerning the software can be avoided with comparative studies. Indeed, this dynamometer's precision and safety were improved more than any other type of conventional disc brake dynamometer.

In this work, we propose an Artificial Intelligence (AI)-based methodology to learn the buckled configuration of stiffeners 3D-printed onto a pre-stretched soft membrane. The membrane acts as a muscle and, if properly pre-deformed, leads to the buckling of the stiffeners so that the resulting configuration can provide new system functionalities. Fused deposition modeling was carried out through a Voron 2.4 3D printer, specifically calibrated for PLA printing on a Lycra fabric. The printed PLA allows a controlled deformation of the substrate–stiffener system; different patterns or stiffeners geometries were investigated, to better understand their effects on the buckled configuration. A finite element model was then set to numerically reproduce the results obtained in the experimental campaign; to catch at best the outcomes, in terms of out-of-plane deflection in the buckled mode, an inverse problem was solved to tune the (nonlinear) constitutive models adopted for PLA and Lycra. Since the numerical model proved to be excessively time-consuming, a surrogate was developed by way of deep learning. In the first stage, YOLO (You Only Look Once) was used and trained properly for feature selection: different geometries of the stiffeners were allowed for and their classification was carried out, in addition to the numerical estimation of their relevant features related to the in-plane geometry. In the second stage, a regression part was added to the AI-based tool to learn the out-of-plane deflection, handled as a label in the learning stage. The results testify to the capability of the proposed approach and its efficiency for subsequent use in the shape optimization of the 3D-printed geometry to attain specific targets of coupled system response.

As is well known in the literature, the use of wide bandgap (WBG)power devices in power conversion units enables higher switching frequencies and lower conduction losses to be achieved, improving their efficiency and power density. Drives used for electric traction are fed by a two-level voltage source inverter (VSI) with a switching frequency not exceeding 30kHz to limit overvoltages at the motor terminals, which can degrade the winding insulation and bearings. The main objective of this research activity is to investigate an alternative solution to VSI-fed drives by using a Current Source Inverter (CSI) based on WBG power devices. The CSI provides a near-sinusoidal output voltage with a significant reduction in dv/dt, thus increasing the motor drive's reliability and improving electromagnetic interference (EMI) immunity. In particular, the working principle of a CSI for electric traction applications is analysed in this paper, and the main strengths and technical challenges are identified and supported by simulations based on Spice model devices and experimental tests. The preliminary simulation results underline that the CSI topology offers significant improvements at high switching frequencies compared to the VSI due to the filtering action provided by suitably combining the design of the dc-link inductor and filter capacitors. Furthermore, the final presentation will include a design analysis of passive elements. The experimental results obtained from tests conducted on a 2kVA test bench will also be presented.

Wire arc additive manufacturing (WAAM) is an additive manufacturing technology that is suitable for large parts and parts with complex shapes such as blades. The area is the topic of research and development for industrial applications. Various studies have revealed the industrial advantages of applying WAAM to part manufacturing compared to traditional fabrication methods. In this study, to further clarify the advantages of industrial applications of WAAM, a study on the repair of impellers that have been damaged by cavitation erosion using WAAM was conducted on turbopumps used in a wide range of industries. A fan-type inducer used in industrial centrifugal pumps was used as a test model. The fan-type inducer was installed in a centrifugal pump experimental apparatus, and then, paint erosion tests were conducted. This test was used to investigate the area of damage to the blades that was caused by cavitation erosion. The results show that the area damaged by cavitation erosion is the trailing edge of the suction tip side of the blade. Based on the paint erosion tests, the machining time for repairing blades with a hybrid system of WAAM and machining were calculated and compared with those required to fabricate a new part. The conclusion was reached that the application of WAAM to the fabrication process of industrial turbopumps has advantages not only in the manufacture of parts, but also in the repair of the parts.

Transient analyses of power devices used in traction motor drives are becoming increasingly challenging because of the use of WBG power switches. Hence, the selection of the sensing plays a key role, as it must be able to accurately measure the critical edges related to the fast transients. A classic method to evaluate the switching losses is the implementation of a double pulse test and computing the energy loss, starting from the measurements of current and voltage waveforms. For this reason, the measurement of the current and voltage should be as accurate as possible to provide good results in this kind of analysis. In this context, the selection of the most appropriate current measurement system can be extremely challenging, since the requirements that must be met are more stringent for SiC- and GaN-based power converters. Indeed, the current sensing system must be compact and non-intrusive to avoid introducing significant parasitic elements, which could influence the transient switching behavior. Furthermore, the current measurement system must guarantee a wide bandwidth, sufficient to capture the fast-switching transient. Moreover, in high power applications such as automotive, current sensing should be characterized by a relevant current range, and it should preferably be isolated to avoid issues in high voltage operations.

This paper aims to investigate the performances of different current measurement systems, considering all the aforementioned requirements. The current measurement achieved by a coaxial shunt resistor, current transformer and Rogowski coil are compared during the performance evaluation of SiC power switches through double pulse tests.

Experimental tests performed on a 1200V – 70A SiC power MOSFET are in progress, and the main goal is to quantify the differences among the current measurement methods under different load and driving conditions, emphasizing their pros and cons. The overall results will be presented in the final presentation.

Long battery charging times is one of the main limits that are currently hindering the widespread adoption of EVs. This issue can be overcome by pursuing solutions with increased charging voltage involving 800 V DC buses. At the same time, traction electric drives are required to feature high efficiency, compactness, a high power density, high reliability and low weight. GaN technology presents a promising opportunity to achieve this target. The use of multilevel inverters is thus imperative to combine the exploitation of this technology with charging voltages above the breakdown voltages of GaN devices. Among multilevel converters topologies, Active Neutral Point Clamped (ANPC) offers the best distribution of switching and conduction devices' losses.

The main aim of this work is to present the design of a modular prototype for a 11kW three-phase 3L-ANPC inverter based on 650 V GaN HEMT devices for electric traction applications. The design consists of a main board comprising the DC bus, DC sensor and the AC output connectors; a chip board with GaN devices, decoupling capacitors and RC snubbers to limit Drain-to-Source overvoltages; a chip board with the driving circuits; and two control boards with optical fiber receivers and STM32 microcontrollers, respectively. The design was carried out by considering a modular approach, which allowed us to choose different control device approaches (with optics or with microcontroller) and different GaN devices’ packages. Moreover, the modularity allowed us to exploit the main board to realize other multilevel topologies by simply redesigning the chip boards.

A further analysis was carried out in such a way as to valuate the parasitics in the overall layout. Design and analysis results will be shown and explained in more detail in the presentation.

Low parasitics allow us to exploit the switching performances of GaN HEMTs, increasing the performance of the multilevel inverter in terms of output distortions and power losses.

The need for Unmanned Surface Vessels (USVs) has been continuously growing in recent years due to the increased demand for automated inspection, surveillance and monitoring platforms. This paper proposes a framework for the development of a modularized, low-cost USV, manufactured with the Fused Filament Fabrication (FFF) technology. The vessel can be operated via Radio Control (RC) and is able to operate autonomously as well. The purpose of this platform is mainly safety applications; however, it is possible to utilize it for scientific tasks, such as water quality monitoring and water structure inspection. Its main advantages are summarized in the modular design, the ability to propel by air and the low build cost. The modular design allows for easy assembly of the vessel, as well as enabling a dynamic size change of the hull. It is noted that the vessel is intended for use in relatively calm waters such as lakes, ponds and reservoirs, especially in waters with dense vegetation. Therefore, the propulsion system was designed with these conditions in mind. Regarding the build cost, the combination of 3D printing, hobby-grade hardware and reliable open-source protocols led to the development of a fully operational and efficient safety platform, with a low cost (<EUR 600). Especially when compared to the cost of commercially available USVs, it delivers a high capability/price (C/P) ratio. Finally, the performance of the platform in terms of buoyancy, stability, steering ability and navigational capabilities was validated through field experimentation in the lake Polyfytos in Kozani, Greece.