Accurate energy production forecasting is critical when planning energy for the economic development of a country. A deep learning approach based on Long Short-Term Memory (LSTM) to forecast one-day-ahead energy production from the run-of-river hydroelectric power plants in Turkey was introduced in the present study. In addition to the LSTM network, three different data-driven methods, namely, adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM), ANFIS with subtractive clustering (SC), and ANFIS with grid partition (GP) were applied. The correlation coefficient (R), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used as quality metrics for prediction. Predicted values of the LSTM, ANFIS-FCM, ANFIS-SC, and ANFIS-GP models were compared with observed values by evaluating their errors. MAPE values in the testing process are 5.98%, 6.14%, 6.16%, and 6.40% for the LSTM neural network, ANFIS-FCM, ANFIS-SC, and ANFIS-GP models, respectively. The comparison revealed that the LSTM neural network provided high accuracy results in one day-ahead short-term energy production prediction and gave higher performance than other ANFIS models used in the study.

The energy that powers electric vehicles comes directly from their high-performance batteries, serving as the heart of their operation. They convert stored chemical energy into mechanical energy to propel vehicles. One of the most vital parts of an electric vehicle is a battery pack. Superior advantages such as higher energy density, longer life cycles, and the fast-charging ability of lithium-ion batteries set them apart from the others. However, battery performance and longevity exhibit a high degree of temperature sensitivity. In other words, operating batteries below and above the specified temperature range values causes problems such as decreased lifespan, safety issues, and performance losses. In electric vehicles, varying power demands during driving cause different current levels to be drawn from the battery packs. This leads to fluctuations in battery temperatures due to chemical reactions occurring. Besides that, regional and seasonal temperature variations also affect the operating temperatures of batteries. Therefore, maintaining the batteries within the specified temperature range, typically between 25 and 40 °C, is only achievable with an adequate battery thermal management system. This review intends to guide researchers working on designing more efficient thermal management systems by providing refined information about previous efforts in this field. The designs found in the literature have been illustrated with simplified figures. Cooling inlet and outlet locations are indicated in blue and red, enabling easier comparison and better understanding of different cooling designs. Air-cooling studies in the literature show that a well-designed system can keep the T_max and ΔT values of LiB cells ~305 K and 2.8 K during 3C discharge at a T_ambient of about 298.15 K. When liquid cooling systems are examined, a 50% glycol–water mixture can maintain pouch cells at nearly 30.3 °C with a ΔT of 2.78 °C under similar 3C and 25 °C conditions. Overall, the results demonstrate that well-designed BTMS configurations including optimized airflow or coolant–flow arrangements are capable of keeping LiBs safely within their optimal thermal operating conditions.

Unmanned air vehicles (UAVs) are used successfully in many different fields mainly various military and civilian applications. Recently electric powered UAVs have become more preferable due to their better accessibility, cost, transportation and silence. However, the batteries used in electric UAVs still do not have enough energy density for long endurance flights. Due to the difficulty of using piston and gas turbine engines in small-sized UAVs, hydrogen fuel cells are seen as one of the considerable options to increase endurance and range. In this study, conceptual design of hybrid UAV including fuel cell, solar unit and battery is presented. Small, light and high endurance UAV was designed using aerodynamic calculations and minimum power requirement for cruise flight was determined. 100 W fuel cell produced by Horizon Fuel Cell Technologies and 3S, 5 Ah of battery were used for main power supplies, moreover solar cells to charge the battery were designed to mount on the surface of the wings. Also, a new power management system is designed for switching between the energy sources. As a result of this work, it is determined that fuel cells and solar units are the significant options which can increase the endurance of UAV up to 8 times depending on the design, solar flux, hydrogen capacity etc.

Temperature is a crucial parameter for ensuring the long lifespan and safe operation of lithium-ion batteries (LiBs). An efficient battery thermal management system (BTMS) tries to maintain temperature in between optimum limits. Despite some disadvantages, air-cooled BTMSs are still preferred due to their advantages such as light weight, simple design, low cost, and ease of maintenance. This study experimentally evaluated a fan-assisted BTMS for the purpose of cooling a 4S2P battery module that includes 18650 type cells. The battery module was initially tested with no cooling system to observe the temperature characteristics of the module, followed by testing with forced air cooling using a fan. Experiments were also conducted with perforated plates installed between the fan and the module to see their effects on the thermal behaviors. Tests were initiated when the ambient temperature was approximately 25 °C and the discharges were carried out by drawing constant currents of 4 A, 8 A, 12 A, and 16 A from the module via an electronic load. The results of this study highlighted the importance of an effective BTMS in ensuring battery safety and performance across different operational conditions. While all tested cooling configurations maintained acceptable temperature levels at lower discharge currents (4 A and 8 A), they struggled to do so at higher currents (12 A and 16 A). Among them, the Fan–HC mode demonstrated the highest efficiency, reducing the maximum temperature (T_max) by 38.82% at 12 A and 28.89% at 16 A compared to the no-cooling scenario. Moreover, it ensured a more uniform temperature distribution within the module. These findings emphasize the necessity of optimized cooling strategies, particularly for high-power applications.

In this paper, the NO_x conversion efficiency of Mo-Cu-Ag/TiO₂/Cordierite catalyst at low temperatures was investigated by using fusel oil as a reductant. The properties of Mo-Cu-Ag/TiO₂/Cordierite catalyst prepared by the impregnation method were analyzed by Brunauer-Emmett-Teller, scanning electron microscopy with energy dispersive spectroscopy, Fourier-transform infrared spectroscopy analyses, and X-ray diffraction. The SCR performance tests were performed under real diesel exhaust gas conditions in a temperature range from 200 °C to 290 °C with a rise of 10 °C, five different engine loads, and three different space velocities. The minimum and maximum NO_x conversion rates were determined as 78,99% at 5 kW and 85.94% at 1 kW engine load, respectively.

In research automotive aerodynamics, it is not common to focus on a specific vehicle due to restricted access to the CAD geometries, their short life span, and limited validation data. For this reason, researchers prefer generic bodies that look like automobiles such as Ahmed Body in their investigations. However, the absence of moving ground and rotating wheels makes these generic bodies unrealistic for aerodynamic studies. In this context, including wheels in CFD simulations, varying ground and wheel boundary conditions, and comparing their qualitative and quantitative flow parameters with the original Ahmed Body experiment is the main objective of this paper. Results have shown that changing stationary ground and wheel boundaries into moving and rotating boundaries do have minor effects on wake characteristics and drag coefficients. However, just the presence of wheels on the model increases force coefficients significantly (increment in drag and lift coefficients by 27.32% and 188.5 counts, respectively.) even though these boundaries are stationary. As a result, the absence of moving ground and rotating wheels can be tolerated to some extent (especially for experimental studies in which inclusion of moving and rotating boundaries may have difficulties). However, a study cannot be evaluated exactly with a model without wheels.