The paper deals with the detection process of energy loss in electric railway hauling vehicles. The importance of efficient energy use in railways and cost-effective rail transport tendency toward regenerative braking energy are considered. In addition, the current situation and improvement opportunities to achieve efficient energy use are examined. Seven measurement series were performed with scheduled Railjet trains between Hegyeshalom and Győr railway stations in Hungary. This railway section is related to the Hungarian State Railways' No. 1 main railway line (between Budapest-Kelenföld and Hegyeshalom state board), which is a part of the international railway line between Budapest and Vienna (capitals of Hungary and Austria, respectively). This double-track, electrified railway line with traditional ballasted superstructures and continuously welded rail tracks is important due to the international passenger and freight transport between Germany, Austria, and Hungary. The value of the regenerative braking energy can be even 20-30% of the total consumed energy. This quite enormous untapped energy can be used for several aims, e.g., for comfort energy demand (air conditioning, heating-cooling, lighting, etc.) or energy-intensive starts. The article also investigates the optimization of regenerative braking energy by seeking the energy-waste locations and the reasons for the significant consumption. The train operator's driving style and habit have been identified as one of the main reasons. Furthermore, train driver assistance systems are recommended to save energy, which is planned for future research.

Due to their high energy density and power potential, 21700 lithium-ion battery cells are a widely used technology in hybrid and electric vehicles. Efficient thermal management is essential for maximizing the performance and capacity of Li-ion cells in both low- and high-temperature operating conditions. Optimizing thermal management systems remains critical, particularly for long-range and weight-sensitive applications. In these contexts, passive heat dissipation emerges as an ideal solution, offering effective thermal regulation with minimal additional system weight. This study aims to deepen the understanding of passive heat dissipation in 21700 battery cells and optimize their performance. Special emphasis is placed on analyzing heat transfer and the relative contributions of convective and radiative mechanisms under varying temperature and discharge conditions. Laboratory experiments were conducted under controlled environmental conditions at various discharge rates, ranging from 0.5×C to 5×C. A 3D-printed polymer casing was applied to the cell to enhance thermal dissipation, designed specifically to increase radiative heat transfer while minimizing system weight and reliance on active cooling solutions. Additionally, a numerical model was developed and optimized using experimental data. This model simulates convective and radiative heat transfer mechanisms with minimal computational demand. The optimized numerical model is intended to facilitate further investigation of the cell envelope strategy at the module and battery pack levels in future studies.

The increasing urgency for sustainable transportation solutions necessitates a thorough examination of energy efficiency within railway systems. This study investigates the energy performance of Siemens Ventus (i.e., Siemens Desiro ML type) electric multiple units on Austria's Raaberbahn network, focusing on route-specific energy consumption and the optimization of regenerative braking. Utilizing data collected from January to May 2023, the research employs a robust methodology that integrates statistical analysis, curve-fitting, and geospatial modeling to assess energy trends along routes connecting Vienna, Bratislava, and Deutschkreutz. The findings reveal that terrain, operational practices, and external environmental factors significantly contribute to energy inefficiencies. Specifically, hotspots of energy overconsumption were identified, leading to the development of tailored optimization models for each route. The analysis also produced heatmaps that illustrate critical spatial and temporal patterns, which are essential for implementing targeted interventions aimed at enhancing energy efficiency.

This research investigates the application of plastic fiber reinforcement in pre-tensioned reinforced concrete railway sleepers, conducting an in-depth examination in both experimental and computational aspects. Utilizing 3-point bending tests and the GOM ARAMIS system for Digital Image Correlation, this study meticulously evaluates the structural responses and crack development in conventional and plastic fiber-reinforced sleepers under varying bending moments. Complementing these tests, the investigation employs ABAQUS’ advanced finite element modeling to enhance the analysis, ensuring precise calibration and validation of the numerical models. This dual approach comprehensively explains the mechanical behavior differences and stresses within the examined structures. The incorporation of plastic fibers not only demonstrates a significant improvement in mechanical strength and crack resistance but paves the way for advancements in railway sleeper technology. By shedding light on the enhanced durability and performance of reinforced concrete structures, this study makes a significant contribution to civil engineering materials science, highlighting the potential for innovative material applications in the construction industry.

Vehicle safety risk can be decreased by diagnosing the lithium-ion battery system of electric road vehicles. Real-time cell diagnostics can avoid unexpected occurrences. However, lithium-ion batteries in electric vehicles can significantly differ in design, capacity, and chemical composition. In addition, the battery monitoring systems of the various vehicles are also diverse, so communication across the board is not available or can only be achieved with significant difficulty. Hence, unique type-dependent data queries and filtering are necessary in most cases. In this paper, a Volkswagen e-Golf electric vehicle is investigated; communication with the vehicle was implemented via an onboard diagnostic port (so-called OBD), and the data stream was recorded. The goal of the research is principally to filter out, identify, and localize defective/weak battery cells. Numerous test cycles (constant and dynamic measurements) were carried out to identify cell abnormalities (so-called deviations). A query and data filtering process was designed to detect defective battery cells. The fault detection procedure is based on several cell voltage interruptions at various loading levels. The methodology demonstrated in this article uses a fault diagnosis technique based on voltage abnormalities. In addition, it employs a hybrid algorithm that executes calculations on measurement and recorded data. In the evaluation, a status line comprising three different categories was obtained by parametrizing and prioritizing (weighting) the individual measured values. It allows the cells to be divided into the categories green (adequate region), yellow (to be monitored), and red (possible error). In addition, several querying strategies were developed accordingly to clarify and validate the measurement results. The several strategies were examined individually and analyzed for their strengths and weaknesses. Based on the results, a data collection, processing, and evaluation strategy for an electric vehicle battery system have been developed. The advantage of the developed algorithm is that the method can be adapted to any electric or hybrid vehicle battery.

The growing number of electric vehicles and devices drives the demand for lithium-ion batteries. The purpose of the batteries used in electric vehicles and applications is primarily to preserve the cells and extend their lifetime, but they will wear out over time, even under ideal conditions. Most battery system failures are caused by a few cells, but the entire system may have to be scrapped in such cases. To address this issue, the goal is to create a concept that will extend the life of batteries while reducing the industrial and chemical waste generated by batteries. Secondary use can increase battery utilization and extend battery life. However, processing a large number of used battery cells at an industrial level is a significant challenge for both manufacturers and users. The different battery sizes and compositions used by various manufacturers of electric vehicles and electronic devices make it extremely difficult to solve the processing problem at the system level. The purpose of this study is to look into non-destructive battery diagnostic options. During the tests, the condition of the cells is assessed using a new diagnostic technique, 3D surface digitalization, and the fusion of electrical parameters. In the case of surface digitalization, the digital image correlation (DIC) technique was used to estimate the cell state. The tests were conducted on various cells with widely used geometries and encapsulations. These included a lithium polymer (soft casing), 18650 standard sizes (hard casing), and prismatic cells (semi-hard). The study also included testing each battery at various charge states during charging and discharging. The findings help to clarify the changes in battery cell geometry and their localization. The findings can be applied to cell diagnostic applications such as recycling, quality assurance, and vehicle diagnostics.