The present study provides a comprehensive overview of energy management strategies (EMS) in urban transportation, with a focus on their application in emerging electric vehicle (EV) technologies, particularly hybrid electric vehicles (HEVs). It presents a comparative analysis of global optimization methods—such as particle swarm optimization (PSO) and genetic algorithms (GA)—and rule-based approaches, including fuzzy logic and Boolean logic. The objective is to evaluate their effectiveness when the electric motor (EM) and internal combustion engine (ICE) operate in hybrid mode, using key performance indicators (KPIs) derived from MATLAB simulations. By determining the most suitable operating mode— whether to use the EM, the ICE, or both—the system aims to optimize energy use, enhance driver experience, reduce ICE dependency and fuel consumption, and support environmental sustainability. The study also explores the potential of emerging technologies like artificial intelligence (AI) and machine learning (ML) to further improve EMS decision-making.

The present paper explores the integration of ISO 50001 energy management standards into engineering education in Europe, aiming to align academic training with the demands of Industry 4.0 (I4.0) and Industry 5.0 (I5.0), as well as the evolving technologies and regulations. It underscores the strategic importance of equipping future engineers with the competencies necessary for energy efficiency, sustainability reporting, and smart sustainable industrial practices. By embedding ISO 50001 into curricula, students can acquire essential skills that enhance their employability, innovation potential, and readiness to contribute to modern energy management systems. The paper also discusses the mutual benefits for companies, including smoother implementation of energy strategies and a workforce better prepared for sustainability-driven transformation. Despite its potential, ISO 50001 adoption remains limited, with many companies still hesitant, highlighting the need for stronger educational-industry collaboration and curriculum reform. However, this approach of academia-industry collaboration could serve as a starting point for initiating the internal audit towards external certification.

The objective of the European and global industrial market for small and medium-sized enterprises (SMEs) is to be sustainable, intelligent, and interconnected in a competitive global market while also addressing climate change. To achieve this goal, the adoption and integration of digital tools have become crucial factors. These technologies can improve efficiency and reduce costs. However, several challenges and barriers have also been identified, including the need for standardization, interoperability, and cybersecurity measures. In light of this, a comprehensive search of academic databases and grey literature was conducted, resulting in the identification of 1010 relevant papers that were analyzed for their insights on the use of digitalization and IoT for SMEs. The findings have important implications for policymakers, industry stakeholders, and researchers working in the field of data management and SMEs.

Efficient energy utilization is vital for sustainable development, and this is particularly crucial for small and medium-sized enterprises (SMEs) in a competitive global market and in the context of combating climate change. Despite the importance of energy flow analysis tools in enhancing energy efficiency and cost-effectiveness for SMEs, there is a lack of knowledge about tools available for this sector. In this vein, this paper aims to explore the factors that influence the adoption and implementation of energy flow analysis tools in SMEs, and to provide guidelines for implementing an internet of things (IoT)-enabled energy monitoring and flow analysis model specifically designed for a SMEs. The effectiveness of the proposed model was demonstrated through a case study that utilized data collected from a company.

As for many industrial sectors, the trend towards a deep grade of digitization of mechatronics systems is ascending considerably year after year. Advantages of digitization are manifold, going from the possibility to test real-case scenarios with cost-effective simulation tools, to the chance to control and optimize real systems by means of their virtual replicas. As a result, enabling technologies such as Digital Twin are in their prime of development. This work intends to contribute to research in this field with a successful application case in a learning factory lab environment. Scope of this work is the modelling, simulation and control of a transfer line, along with the mechatronic equipment operating on it. The simulated system will complete the functionalities and potentialities of the virtual replica of the line in the framework of a Digital Twin-enabled demonstrator. Limitations and future improvements of this work-in-progress activity are also highlighted.