Photovoltaic (PV) modules suffer from a reduction of electric conversion due to the high operating temperatures of the PV cells. Hybrid photovoltaic/thermal (PV/T) technology represents an effective solution for cooling the PV cells. This paper discusses a theoretical study on a novel bi-fluid PV/T collector. One dimensional steady-state numerical model is developed, and computer simulations are performed using MATLAB. This numerical model is based on a pilot PV/T plant, installed in the Campus of the University of Catania, and was experimentally validated. The design of the proposed bi-fluid PV/T is based on a commercial WISC PV/T collector, to which are added an air channel, an aluminum absorber with fins, and a layer of thermal insulation. The analysis of the thermal behavior of the proposed collector is carried out as a function of the flow rate of the two heat transfer fluids (air and water). Finally, the comparative analysis between the conventional water-based PV/T collector, namely PV/T, and the bi-fluid (water/air-based) WISC PVT, namely PV/Tb, is presented for both winter and summer days. For the investigated winter day, the numerical results show an overall improvement of the performance of the bi-fluid PV/T module, with an increase of thermal energy transferred to the liquid side of 20%, and of 15.3% for the overall energy yield in comparison to the conventional PV/T collector. Instead, a loss of 0.2% of electricity is observed. No performance improvements were observed during the summer day.

The production of electric energy has been increasingly deriving from renewable sources, and it is projected that this trend will continue over the next years. Among these sources, the use of solar energy is supposed to be considered the main future solution to global climate change and fossil fuel emissions. Since current photovoltaic (PV) panels are estimated to have an average life of 25–30 years, their disposal is very important for the recovery of materials already used and for introducing them again into other processing cycles. Innovative solutions are therefore needed to minimize the emissions of pollutants derived from the recycling of photovoltaic panels that no longer work. In this research, an analysis of data related to durability, recyclability rates, different possible design layouts and materials used in the design and manufacture of PV panels was conducted. Through a Design for Recycling (DfR) and a Design for Durability (DfD), the authors identified the optimal materials, the best geometries and geometric proportions as well as the most convenient geometric and dimensional tolerances in the couplings between the layers and the components that comprise the panel to attain the most current, efficient and effective solutions for recycling end-of-life (EoL) PV panels and for longer durability.

This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy is quantitatively evaluated, and their effectiveness measured. The results demonstrate that both XGB and ETR models consistently outperform KNN in accurately predicting both electrical and thermal efficiency. Specifically, the XGB model achieves remarkable correlation coefficient (R²) values of approximately 0.99999, signifying its superior predictive capabilities. Notably, the XGB model exhibits a slightly superior performance compared to ETR in estimating electrical efficiency. Furthermore, when predicting thermal efficiency, both XGB and ETR models demonstrate excellence, with the XGB model showing a slight edge based on R² values. Validation against new data points reveals outstanding predictive performance, with the XGB model attaining R² values of 0.99997 for electrical efficiency and 0.99995 for thermal efficiency. These quantitative findings underscore the accuracy and reliability of the XGB and ETR models in predicting the electrical and thermal efficiency of PVT systems when cooled by nanofluids. The study’s implications are significant for PVT system designers and industry professionals, as the incorporation of AI-based models offers improved accuracy, faster prediction times, and the ability to handle large datasets. The models presented in this study contribute to system optimization, performance evaluation, and decision-making in the field. Additionally, robust validation against new data enhances the credibility of these models, advancing the overall understanding and applicability of AI in PVT systems.

Photovoltaic/thermal (PV/T) systems are innovative cogeneration systems that ensure the cooling of photovoltaic (PV) backside and simultaneous production of electricity and heat. However, an effective cooling of the PV back is still a challenge that affects electrical and thermal performance of the PV/T system. In the present work, a PV/T numerical model is developed to simulate the heat flux based on energy balance implemented in MATLAB software. The numerical model is validated through the comparison of the three-layer PV model with the NOCT model and tested under the operation conditions of continental temperate climate. Moreover, the effect of velocity and water film thickness as important flow parameters on heat exchange and PV/T production is numerically investigated. Results revealed that the PV model is in good agreement with the NOCT one. An efficient heat transfer is obtained while increasing the velocity and water film thickness with optimal values of 0.035 m/s and 7 mm, respectively, at an inlet temperature of 20 °C. The PV/T system ensures a maximum thermal power of 1334.5 W and electrical power of 316.56 W (258.8 W for the PV). Finally, the comparison between the PV and PV/T system under real weather conditions showed the advantage of using the PV/T.

The increasing depletion of fossil fuels and their environmental impact have led to the development of fuel cell hybrid electric vehicles. By combining fuel cells with batteries, these vehicles offer greater efficiency and zero emissions. However, their energy management remains a challenge requiring advanced strategies. This paper presents a comparative study of two developed energy management strategies: a deterministic rule-based approach and a fuzzy logic approach. The proposed system consists of a proton exchange membrane fuel cell (PEMFC) as the primary energy source and a lithium-ion battery as the secondary source. A comprehensive model of the hybrid powertrain is developed to evaluate energy distribution and system behaviour. The control system includes a model predictive control (MPC) method for fuel cell current regulation and a PI controller to maintain DC bus voltage stability. The proposed strategies are evaluated under standard driving cycles (UDDS and NEDC) using a simulation in MATLAB/Simulink. Key performance indicators such as fuel efficiency, hydrogen consumption, battery state-of-charge, and voltage stability are examined to assess the effectiveness of each approach. Simulation results demonstrate that the deterministic strategy offers a structured and computationally efficient solution, while the fuzzy logic approach provides greater adaptability to dynamic driving conditions, leading to improved overall energy efficiency. These findings highlight the critical role of advanced control strategies in improving FCHEV performance and offer valuable insights for future developments in hybrid-vehicle energy management.

The development of ion-sensitive field-effect transistor (ISFET) sensors based on silicon nanowires (SiNW) has recently seen significant progress, due to their many advantages such as compact size, low cost, robustness and real-time portability. However, little work has been done to predict the performance of SiNW-ISFET sensors. The present study focuses on predicting the performance of the silicon nanowire (SiNW)-based ISFET sensor using four machine learning techniques, namely multilayer perceptron (MLP), nonlinear regression (NLR), support vector regression (SVR) and extra tree regression (ETR). The proposed ML algorithms are trained and validated using experimental measurements of the SiNW-ISFET sensor. The results obtained show a better predictive ability of extra tree regression (ETR) compared to other techniques, with a low RMSE of 1 × 10⁻³ mA and an R² value of 0.9999725. This prediction study corrects the problems associated with SiNW -ISFET sensors.