József Menyhárt

Enhancing manufacturing efficiency increasingly relies on integrating Overall Equipment Effectiveness (OEE), Industry 4.0 technologies, and Artificial Intelligence (AI) with Machine Learning (ML). Traditionally, OEE aggregates Availability, Performance, and Quality, but in Industry 4.0 it can evolve into a real-time decision-support tool enriched with predictive analytics. This paper provides a conceptual systematic synthesis of the interplay between OEE and AI-driven methodologies, emphasizing the role of fuzzy logic and hybrid models in managing uncertainty. AI/ML applications—predictive maintenance, quality assurance, and process optimization—reduce downtime, minimize scrap, and increase productivity by improving OEE components through pattern recognition and forecasting. A further research focus is domain shift and transfer learning, which impact the scalability and robustness of industrial AI systems across changing equipment and factories. Transfer learning approaches such as fine-tuning, feature alignment, and adversarial adaptation can reduce retraining effort while maintaining performance. Finally, integrating AI with OEE supports sustainable manufacturing by improving energy efficiency and reducing environmental impacts, contributing to the long-term vision of self-optimizing digital factories capable of responding dynamically to market and environmental changes.

The rapid spread of electromobility and renewable energy sources is fundamentally transforming contemporary energy systems. The Vehicle-to-Grid (V2G) technology offers a new role for electric vehicles to function as distributed energy storage units, supporting grid stability and the integration of renewable energy into everyday energy use. The aim of the study is to apply a SWOT analysis to identify the main strengths, weaknesses, opportunities, and threats associated with the introduction of V2G technology in the European Union, with a focus on Hungary in Eastern Europe. The analysis highlights that the primary strengths of V2G systems lie in EU-level regulatory support and the growing electric vehicle fleet, while weaknesses include limited charging infrastructure, technological uncertainty, and economical aspects like GDP. Opportunities include energy communities, secondary battery use, and the development of smart grid solutions, while regulatory delays and high investment costs are identified as the main threats. The results of the research confirm that the successful implementation of V2G technology is only possible with a complex approach that addresses technological, economic, and social aspects.

This theory-oriented, methodological and conceptual survey explores the application of Multi-Criteria Decision-Making (MCDM) methods in vehicle routing, transportation and robot navigation. Multi-criteria decision aid brings significant added value in applications where robots and autonomous agents execute missions with multiple objectives in multi-cost and multi-criteria environments. Logistic operations and multi-modal transportation tasks, where multiple factors influence mission success, also benefit from multi-criteria decision aid. Decisions are inherently complex, and MCDM methods capture certain aspects of the decision contexts as they inherently encode different contextual priorities. An evaluation on the effectiveness of MCDM methods may prove to be uninformative without information on contextual preferences. Direct MCDM method comparison may fail to reveal insight over method effectiveness without contextual information. The survey presents an interpretive synthesis over decision modeling constructs and introduces a new context-oriented analytical synthesizing perspective to establish a meaningful base for comparison. Through conceptual abstraction over decision roles, latent decision structures and recurring decision patterns, two stable explanatory constructs emerged: ‘governing aspects’and ‘application attitudes’. Governing aspects characterize decision influences, whereas application attitudes characterize decision architectures. We analyze attitudes pertaining to different application domains from the perspective of responsiveness and computation demand, and discuss some key governing aspects, such as robust decision-making and behavior elicitation. We aim to provide a rich landscape of multi-criteria decision scenarios, and identify future research areas based on our findings. The outlined synthesizing framework functions both as a methodological taxonomy and a conceptual compass and case repository for navigating MCDM applications.

The Vehicle-to-Grid (V2G) technology is considered one of the best solutions for integrating renewable energy systems; however, most literature reports favorable economic results using synthetic data, without accounting for seasonal or market limitations. The current research presents the results of the MATLAB R2023b (Version 23.2, MathWorks, Natick, MA, USA) simulation of the 100-household energy community in Debrecen, Hungary, with 30 electric vehicles (EVs) using entirely simulation-based Lithium Iron Phosphate (LiFePO4) batteries, a simulation-based 150 kW solar photovoltaic (PV) system, and a simulation-based 200 kW wind power system, using real meteorological data for January 2024. The optimization of charging/discharging for electric vehicles was performed using a multi-objective genetic algorithm (GA) over 30 days at a 15 min time resolution, accounting for stochastic loads and temperature effects on battery degradation, with a sensitivity analysis of key parameters. The results of the optimized solution for the electric vehicle charging/discharging were unexpected: the total energy cost increased by 68.9% ($4337.65 to $7327.54), the peak demand increased by 266.2% (31.9 to 116.9 kW), the degradation cost was $479.63, the load factor was reduced from 0.847 to 0.722, and the SOC constraint was violated for 0.758% of measurements. The V2G is not economically viable under current Hungarian pricing and Central Europe winter conditions. Results are robust for varying parameters using sensitivity analysis and Pareto front tracing. The break-even point is achieved when ratios of peak-to-off-peak prices are above 3.5:1. Seasonal policies and market reforms are critical for V2G viability. Importantly, the influence of inherent design deficiencies in the optimization model on the reported results cannot be ruled out.

The European Union’s Green Deal and decarbonisation objectives are increasing the pressure to enhance the efficiency and integration of renewable energy systems. More and more in the commercial sectors, photovoltaic (PV) systems are being adopted. But it is not as easy to adopt it as this. The reason for this is load variation and solar intermittency. At the same time, electric vehicles (EVs) can enable vehicle-to-grid (V2G) integration, which allows for bidirectional power flows that enhance grid stability and energy flexibility. This review assesses whether using PV systems in V2G technologies in commercial applications can improve energy efficiency and curb CO₂ emissions. This paper discusses the enablers and barriers for the deployment of PV + V2G systems along technical, environmental, and policy dimensions through reviewing recent research. In conclusion, it provides insights into future research avenues that could potentially boost this promising trajectory of the EU energy transition.

The rapid development and spread of electric vehicles is fundamentally revolutionizing transportation in the European Union and around the world. With the diffusion of electric vehicles, issues related to the batteries that power them have also become more prominent. Given that the production of these components is one of the most environmentally burdensome processes, the need for their secondary use has quickly become evident. Based on the Eurostat database, this article analyzes the indicators that may influence the prospects for the secondary use of batteries. It examines the relationship between the GDP (Gross Domestic Product) of European Union member states and the number of electric vehicles, the share of renewable energy, and household electricity consumption. The results show that electric vehicle penetration and the use of renewable energy vary greatly among EU member states. The second part of the article examines battery data from an electric vehicle, the solar panel production of a family home, and electricity consumption using a linear programming model on a monthly basis. The objective function of the model makes it possible to minimize the amount of energy purchased from the grid. The resulting savings can be quantified. The article focuses on providing a foundation for the opportunities offered by the secondary-use battery market.

This study evaluates deep learning models for vehicle detection in urban environments, focusing on the integration of regional data and standardized evaluation protocols. A central contribution is the creation of DebStreet, a novel dataset that captures images from a specific urban setting under varying weather conditions, providing regionally representative information for model development and evaluation. Using DebStreet, four state-of-the-art architectures were assessed: Faster R-CNN, YOLOv8, DETR, and Side-Aware Boundary Localization (SABL). Notably, SABL and YOLOv8 demonstrated superior precision and robustness across diverse scenarios, while DETR showed significant improvements with extended training and increased data volume. Faster R-CNN also proved competitive when carefully optimized. These findings underscore how the combination of regionally representative datasets with consistent evaluation methodologies enables the development of more effective, adaptable, and context-aware vehicle detection systems, contributing valuable insights for advancing intelligent urban mobility solutions.

This study, aimed at professionals in research and development in the fields of computer vision, artificial intelligence, and intelligent transportation, presents a systematic literature review on recent machine learning methodologies applied to the detection and tracking of vehicles, pedestrians, and traffic flow. The analysis of articles published between 2022 and 2025 (early access) in the post-COVID era explored the integration of machine learning and deep learning to address traffic challenges, allowing for the comparison of different approaches and the formulation of hypotheses based on the 46 articles that comprised the review corpus. Furthermore, the evaluation of the reported metrics revealed inconsistencies in the methodologies employed, attributed to the lack of standardization across the studies. In light of this, this work proposes alternatives for future experiments, emphasizing the emerging potential of the field through the adoption of new standardization systems and the exploration of experimental combinations.

Renewable energy sources and energy independence are becoming increasingly important worldwide, and reducing emissions and optimizing energy use are high on the EU’s agenda. In this context, electric and hybrid vehicles could not only be a means of transport but also an active part of the grid. This paper analyzes one year of empirical data of a hybrid vehicle using a linear programing method that allows the optimization of energy return under different settings. The aim of the study is to determine the contribution that vehicles can make to the stability of the grid and the functioning of energy communities. It also compares the distribution of energy sources used in the EU and presents the current range of V2G-capable vehicle models. The results show that hybrid vehicles can also be effective energy storage devices, especially at fleet level. V2G technology could influence the development of battery production and contribute to the expansion of secondary markets by enabling the recycling of degraded batteries for buildings or renewable energy systems. The article also summarizes the development opportunities and challenges for V2G technology, in particular its role in energy grids and sustainable transport.