The paper presents a mathematical model that characterizes the process of threshing and separation from the threshing machine with an axial flow of a thresher, taking into account the following input parameters: material flow, rotor speed, distance between rotor and counter rotor, mean density of processed material, feed speed, length of thresher and separating surface. Output parameters, such as the distribution function of separated seeds, distribution density function of separated seeds and distribution function of free seeds in the threshing space, as well as the distribution function of unthreshed seeds, together with the value of evacuation losses, were used to control the modeling process.

Greenhouses have taken on a fundamental role in agriculture. The Internet of Things (IoT) is a key concept used in greenhouse-based precision agriculture (PA) to enhance vegetable quality and quantity while improving resource efficiency. Integrating wireless sensor networks (WSNs) into greenhouses to monitor environmental parameters represents a critical first step in developing a complete IoT solution. For further optimization of the results, including actuator nodes to control the microclimate is necessary. The greenhouse must also be remotely monitored and controlled via an internet-based platform. This paper proposes an IoT-based architecture as a decision support system for farmers. A web platform has been developed to acquire data from custom-developed wireless sensor nodes and send commands to custom-developed wireless actuator nodes in a greenhouse environment. The wireless sensor and actuator nodes (WSANs) utilize LoRaWAN, one of the most prominent Low-Power Wide-Area Network (LPWAN) technologies, known for its long data transmission range. A real-time end-to-end deployment of a remotely managed WSAN was conducted. The power consumption of the wireless sensor nodes and the recharge efficiency of installed solar panels were analyzed under worst-case scenarios with continuously active nodes and minimal intervals between data transmissions. Datasets were acquired from multiple sensor nodes over a month, demonstrating the system’s functionality and feasibility.

Batteries have become the main power source in today’s automotive systems. This paper proposes the design of a fast-balancing passive battery management system (BMS) with remote monitoring for the automotive domain. This system is designed for four series-connected lithium iron phosphate (LiFePO4) cells, which are the preferred choice in the automotive industry. The results show that the proposed BMS can monitor the cell voltages with an error lower than 0.12%, and it can perform the balancing operation successfully with maximum currents of 750 mA during both charging and discharging cycles, not only for LiFePO4 cells, but also for lithium-ion (Li-ion) cells. Furthermore, the cell voltages are sent over the controller area network (CAN) interface for remote monitoring.

In the automotive domain, the vast majority of testing is performed through simulations, which can validate a system design before the actual implementation and can emphasize eventual faults in the design process. Hence, the simulation is of utmost importance. Behavioral models are necessary for the creation of each electronic device desired in the system, and some of the components have very complex behavior: low-dropout linear voltage regulators (LDOs), gate drivers, and switching regulators. In the automotive industry, LDOs are essential components because they power all the other subsystems and very accurate behavior is needed to make sure that the system behaves as in reality. LDO models are already commercially available and most of their intrinsic characteristics are modeled (dropout voltage, line regulation, load regulation, etc.). However, one characteristic that is extremely useful, yet the hardest to model, is the power supply rejection ratio (PSRR). This paper proposes a new PSRR modeling technique for automotive low-dropout voltage regulators. The new PSRR characteristic was modeled for an automotive LDO product in a Texas Instruments portfolio, which has a commercially available model, and was simulated using the PSpice Allegro simulator and the OrCAD Capture CIS environment.

Nowadays, electronic circuits’ time to market is essential, with engineers trying to reduce it as much as possible. Due to this, simulation has become the main testing concept used in the electronics domain. In order to perform the simulation of a circuit, a behavioral model must be created. Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) are semiconductor devices found in a multitude of electronic circuits, and they are also used as power switches in many applications, such as low-dropout linear voltage regulators, switching regulators, gate drivers, battery management systems, etc. A MOSFETs’ behavior is extremely complex to model, thus, creating high-performance models for these transistors is an imperative condition in order to emulate the exact real behavior of a circuit using them. An essential parameter of MOSFET power switches is the ON-state resistance (RDS_ON), because it determines the power losses during the ON state. Ideally, the power losses need to be zero. RDS_ON depends on multiple factors, such as temperature, load current, and gate-to-source voltage. Previous studies in this domain focus on the modeling of the MOSFET only in specific operating points, but do not cover the entire variation range of the parameters, which is critical for some applications. For this reason, in this paper, there was introduced for the first time a novel ON-state resistance modeling technique for MOSFET Power Switches, which solves the entire RDS_ON dependency on the transistor’s variables stated above. The novel RDS_ON modeling technique is based on modulating the transistor’s gate-to-source voltage such that the exact RDS_ON value is obtained in each possible operating point. The method was tested as a real-life example by creating a behavioral model for an N-channel MOSFET transistor and the chosen simulation environment was Oregon, USA, Computer-Aided Design (OrCAD) capture. The results show that the model is able to match the transistor’s RDS_ON characteristics with a maximum error of 0.8%. This is extremely important for applications in which the temperatures, voltages, and currents vary over a wide range. The new proposed modeling method covers a gap in the behavioral modeling domain, due to the fact that, until now, it was not possible to model the RDS_ON characteristics in all operating corners.

This study investigates the temperature-dependent electrical behavior of 400 F/2.7 V supercapacitors, both at the single cell level and in packs of six cells connected in series (6S configuration), over the temperature range of −20 °C to 58 °C, for further development of sustainable energy storage systems (ESSs). In the context of the increasing share of renewable energy sources and the development of low-environmental-impact storage systems, supercapacitors represent a viable technology due to their high power density, energy efficiency, and extended operational lifespan. The work is motivated by the critical influence of temperature on the Equivalent Series Resistance (ESR), voltage stability, and charge–discharge dynamics in real energy storage applications. Experimental characterization was performed using controlled Constant-Current (CC) and Constant-Voltage (CV) charge–discharge cycles with automatic data acquisition, complemented by temperature regulation via thermal chambers. Different parameters like ESR, Open-Circuit Voltage (OCV), and transient current profiles were extracted for each cell, while the pack-level behavior was derived by analyzing the experimental data. Additional investigations have included voltage recovery analysis. The results showed that the capacitance remains largely stable over the entire temperature range investigated, while the ESR exhibits a strong nonlinear dependence, increasing significantly at low temperatures and contributing to a reduced efficiency. A common transition region was identified around the 20–25 °C range, where the system performance is optimal. The voltage recovery increases with temperature, indicating increased ionic mobility and faster charge redistribution. Significant non-uniformity was observed between cells connected in series, with some cells exhibiting higher thermal sensitivity and instability. Optimizing the use of supercapacitors within their operating temperature range may contribute to increasing energy efficiency, to extending system lifespan, and to developing sustainable solutions for modern ESSs. In conclusion, the 6S supercapacitor pack exhibits a strongly temperature-dependent electrochemical dynamics, governed mainly by the ESR and diffusion processes. These findings highlight the need for thermal modeling and integration of balancing strategies in series-connected supercapacitor packs, which can be used for the expansion of the environmental-friendly ESSs.