The vehicle testing–validation phase is a crucial and demanding task in the automotive development process for vehicle manufacturers. It ensures the correct operation, safety, and efficiency of the vehicle. To meet this demand, some commercial solutions are available on the market, but they are usually expensive, have few connectivity options, and are PC-dependent. This paper presents an IoT-based intelligent low-cost system for vehicle data acquisition during on-road tests as an alternative solution. The system integrates low-cost acquisition hardware with an IoT server, collecting and transmitting data in near real-time, while artificial intelligence (AI) algorithms process the information and report errors and/or failures to the manufacturing engineers. The proposed solution was compared with other commercial systems in terms of features, performance, and cost. The results indicate that the proposed system delivers similar performance in terms of the data acquisition rate, but at a lower cost (up to 13 times cheaper) and with more advanced features, such as near real-time intelligent data processing and reduced time to find and correct errors or failures in the vehicle.

Solar radiation and human activity generate ubiquitous temperature gradients that could be harvested by thermoelectric generators (TEGs). However, most of these temperature gradients are in the range of very few degrees and, while TEGs are able to harvest them, the resulting output voltages are extremely small (a few hundreds of mV), and DC–DC converters are necessary to boost them to usable levels. Impedance matching between TEGs and DC–DC converter plays a fundamental role in the energy harvesting efficiency. Therefore, it is essential to determine the output power of the system in different configurations, in order to decide on the optimum TEG connection. Here, we present an electronic circuit to measure the maximum power that can be harvested with low-voltage TEGs connected to a DC–DC converter. The developed circuit is an electronic controlled load that drains the maximum current from the output of the DC–DC converter while maintaining its output voltage at the maximum allowed value. Using a mechanical set-up able to apply precise low temperature gradients between the hot and cold side of the TEGs, experimental data using different configurations of TEGs are obtained. The measured results show that, for ultra-low voltages, the TEG ensemble’s output impedance plays an important role not only in the amount of the energy scavenged, but also in the onset temperature of the energy harvesting.

This work aims to develop a prototype for autonomously monitoring highway weather conditions using thermoelectric energy. The prototype utilizes solar thermal energy, heat absorbed by the asphalt and soil heat to generate, convert, manage, and store energy. The study investigates the optimal functioning of the thermoelectric generator, including utilizing the soil as a heat sink and incorporating passive thermal control devices like heat pipes. A commercial ultra-low power converter model LTC3108 was experimentally tested for efficient energy conversion. The monitoring system is designed to acquire readings from sensors measuring relevant climate parameters such as temperature, atmospheric pressure, and humidity, focusing on ultra-low power characteristics. The acquired data is transmitted via LoRa technology to a server for processing and analysis. The prototype generated 286.24 J of energy with a maximum temperature gradient of 4.25 ºC in the thermoelectric generator. The energy cost of transmitting a 40-byte message through the LoRaWAN module was 0.625 J. The proposed climate data acquisition circuit had a theoretical consumption of 17.69 J. Experimental tests with the LTC3108 converter, and a 0.22 F supercapacitor demonstrated successful data transmission. This work provides a solid foundation for future studies utilizing thermoelectric energy in highway applications, resulting in a prototype suitable for real-world road scenarios.

In the oil and gas industry, heat exchangers are subject to loads that cause malfunctioning. These loads are divided into thermal and mechanical stresses; however, most efforts are focused on studying thermal stresses. The present work reduces mechanical stresses by mitigating pressure events in a gasket plate heat exchanger (GPHE). GPHE requires that the hot and cold stream branches have approximately the same pressure. Thus, the work focuses on controlling the pressure difference between the branches. A test bench was used to emulate, on a small scale, the typical pressure events of an oil production plant. A control valve was used in different positions to evaluate the controller. In the experiments, it was observed that the best option to control the pressure difference is to use a hydraulic pump and control valve in the flow of the controlled thermal fluid branch. The reduction in pressure events was approximately 50%. Actuator efforts are also reduced in this configuration.

Recent trends indicate an increase in the number of small satellite missions, which can be developed more quickly and at a lower cost than traditional satellites. This has led to a growing interest in university-based satellite development, despite a lack of expertise in the space field, which has resulted in a high failure rate for such missions. To address this issue, the implementation of robust and reliable verification and validation (V&V) methods has become essential, and it has been demonstrated that the use of a FlatSat during the V&V campaign increases reliability. Despite the significance of FlatSat, there is a dearth of information on the platforms used to implement it, as well as a classification scheme for locating them. This article contributes to bridging this gap by conducting a systematic mapping of 65 works that were selected based on specific criteria and subsequently analyzed. The primary characteristics of the platforms are enumerated, and a new classification for FlatSat platforms into Raw, Bridge, Dock, and Modular is proposed. In order to provide a comprehensive understanding of the topic, the principal tests conducted on these platforms were also covered.

Electricity consumption is growing rapidly worldwide. Renewable energy resources, such as solar energy, play a crucial role in this scenario, contributing to satisfy demand sustainability. Although the share of Photovoltaic (PV) power generation has increased in the past years, PV systems are quite sensitive to climatic and meteorological conditions, leading to undesirable power production variability. In order to improve energy grid stability, reliability, and management, accurate forecasting models that relate operational conditions to power output are needed. In this work we evaluate the performance of regression methods applied to forecast short term (next day) energy production of a PV Plant. Specifically, we consider five regression methods and different configurations of feature sets. Our results suggest that MLP and SVR provide the best forecasting results, in general. Also, although features based on different solar irradiance levels play a key role in predicting power generation, the use of additional features can improve prediction results.