Electric vehicle (EV) technology is emerging as one of the most promising solutions for green transportation. The same growth occurs in the charging infrastructure development and automating the EV charging process. Globally, EVs has different types of charging sockets and it’s located at the various positions in the Vehicle. In simple, EV has a diversity in socket type and socket location. Hence, correctly identifying the socket type and location is mandatory to automate the charging process. The recent development in computer vision and robotic systems helps to automate EV charging without human intervention. Image processing and deep learning-based socket identification can help the EV charging infrastructure providers automate the process. Moreover, the deep learning techniques should be simple enough to implement in the real-time processing boards for experimental viability. Hence, this paper proposes a new You Only Look Once (YOLO) model called the Electric Vehicle Socket (EVS) YOLO that uses YOLOv5 as its base architecture with the addition of a vision-type transformer called the SWIN-Transformer and an attention mechanism called SimAM for better performance of the model in detecting the correct charging port. A dataset of 2700 images with six types of classes has been used to test the model, and the EVS -YOLO also evaluated with varying mechanisms of attention positioned at various places along the head. The paper contrasts the suggested model with alternative deep learning architectures and analyzes respective performances.

Hydrogen is one of the modern energy carriers, but its storage and practical use of the newest hydrogen technologies in real operation conditions still is a task of future investigations. This work describes the experimental hydrogen hybrid energy system (HHS). HHS is part of a laboratory off-grid system that stores electricity gained from photovoltaic panels (PVs). This system includes hydrogen production and storage units and NEXA Ballard low-temperature proton-exchange membrane fuel cell (PEMFC). Fuel cell (FC) loses a significant part of heat during converting chemical energy into electricity. The main purpose of the study was to explore the heat distribution phenomena across the FC NEXA Ballard stack during load with the next heat transfer optimization. The operation of the FC with insufficient cooling can lead to its overheating or even cell destruction. The cause of this undesirable state is studied with the help of infrared thermography and computational fluid dynamics (CFD) modeling with heat transfer simulation across the stack. The distribution of heat in the stack under various loads was studied, and local points of overheating were determined. Based on the obtained data of the cooling air streamlines and velocity profiles, few ways of the heat distribution optimization along the stack were proposed. This optimization was achieved by changing the original shape of the FC cooling duct. The stable condition of the FC stack at constant load was determined.

In the manufacture squirrel-cage rotors of asynchronous motors, a high standard of quality is required in every part of the production cycle. The die casting process usually creates porosity in the rotor bars. This most common defect in the rotor often remains hidden during the entire assembly of the machine and is usually only detected during final testing of the motor, i.e., at the end of the production process. This leads to unnecessary production costs. Therefore, the aim is to conduct a continuous control immediately after the rotor has been cast before further processing. In our paper, we are interested in selecting a suitable method of offline rotor diagnostics of an asynchronous motor that would be effective for these needs. In the first step, the selection of the method and its integration into the overall manufacturing process is carried out. The arrangement of the sensors and their calibration is then simulated on a 2D Finite Element Model of the rotor. The proposed offline measurement procedures and technologies are finally validated by testing measurements on a rotor that simulates the most frequently occurring faults. A test system is also developed that provides the operator continuous information about the running rotor measurements and makes it easier to evaluate the quality of the cast rotor by means of graphical visualization of the faults.

The article focuses on a giga-watt scale hydrogen factory. This factory will be in the USA, where the investor requested a study from ABB about the feasibility of this project. First, the article discusses the technology of electrolyzers which will be used. The second chapter focuses design of this giga-watt factory. The third chapter describes the block flow diagram and how each variance of the factory should look. And last section of this paper is about site selection and plot plan.

Currently, there is a rapid increase in the installed capacity of photovoltaic (PV) power plants around the world. However, with the increasing number of PV plants, it is necessary to keep in mind their safe and reliable operation. As photovoltaic panels gradually age, they degrade and develop various defects that can result in reduced performance or even shut down the plant. This causes financial losses. To detect these faults early, there are several diagnostic methods that allow early detection, thereby increasing the efficiency of the operation. The most commonly used methods of PV panel diagnostics are described in this paper. A major part of the text is devoted to electroluminescence. The main part deals with the easy modification of a commercial camera for the purpose to perform diagnostics in low-cost conditions.