The Lithium-Ion battery at the end of life represents a valuable source of secondary raw materials such as lithium, nickel, cobalt etc. Deep discharging, as a part of battery recycling, is a time-consuming process in which the battery's thermal dependency on the discharge parameters and voltage recovery effect is manifested. Adjusting the discharging process adequately to address those two phenomena leads to a safety increase and discharging time decrease. This paper treats two aspects. It is observed the effect of the constant and variable discharging current along with the depth of the discharge in the form of discharge end voltage parameter on the maximal cell temperature reached during the process. The second aspect is the battery recovery voltage trend after the discharging process and its dependency on the same parameters. The impact of these parameters is demonstrated experimentally on two battery cell types.

In our recent attempt to explain flavor hierarchies [1], a gauged SU(2) flavor symmetry acting on left-handed fermions provides a ground to introduce three independent rank-one contributions to the Yukawa matrices: a renormalizable one for the third family, a mass-suppressed one for the second family, and an additional loop-suppressed factor for the first family. Here, we demonstrate how minimal quark-lepton unification à la Pati-Salam, relating down-quarks to charged leptons, can significantly improve this mechanism. We construct and thoroughly analyze a renormalizable model, performing a comprehensive one-loop matching calculation that reveals how all flavor hierarchies emerge from a single ratio of two scales. The first signatures may appear in the upcoming charged lepton flavor violation experiments.

A fast-paced policy context is characteristic of energy and climate research, which strives to develop solutions to wicked problems such as climate change. Funding agencies in the European Union recognize the importance of linking research and policy in climate and energy research. This calls for an increased understanding of how stakeholder engagement can effectively be used to co-design research questions that include stakeholders' concerns. This paper reviews the current literature on stakeholder engagement, from which we create a set of criteria. These are used to critically assess recent and relevant papers on stakeholder engagement in climate and energy projects. We obtained the papers from a scoping review of stakeholder engagement through workshops in EU climate and energy research. With insights from the literature and current EU climate and energy projects, we developed a workshop programme for stakeholder engagement. This programme was applied to the European Climate and Energy Modelling Forum project, aiming to co-design the most pressing and urgent research questions according to European stakeholders. The outcomes include 82 co-designed and ranked research questions for nine specific climate and energy research themes. Findings from the scoping review indicate that papers rarely define the term 'stakeholder'. Additionally, the concepts of co-creation, co-design, and co-production are used interchangeably and often without definition. We propose that workshop planners use stakeholder identification and selection methods from the broader stakeholder engagement literature.

The consumers with building integrated photovoltaic (PV) systems have become prosumers, and their profit depends on network regulations, especially in the treatment of surplus electricity. Net-metering and feed-in tariff are the most common remuneration mechanisms for prosumers. Increasing the number of prosumers can cause various technical problems in the grid, therefore the distribution system operator sometimes imposes legal/regulatory and technical restrictions that are reflected in zero energy export. Integration of the energy storage systems can help with problems arising from these restrictions, but will make the initial investment significantly more expensive. This may negatively affect the profitability of investment. The main aim of this paper is analysis of different regulatory policies and their impact on building integrated PV system profitability. Two profitability metric factors were calculated for the purpose of better policy comparison. For the presented analysis, real data sets of a load demand and PV energy production were used. As an example, the integrated PV system installed at the Faculty of EE University of Sarajevo is analyzed.

Building integrated microgrids (BIMs) present a promising step towards a more efficient, decentralized and sustainable power system. Many buildings already have various renewable energy sources (RES) integrated, but the next step is adding energy storage (ES) systems, or proactive loads such as electric vehicles (EVs) to an already established system. However, ensuring the resilience of the system to accept these new elements presents a challenge in terms of stability, efficiency, and operational capability. This paper focuses on size optimized BIM simulated on Typhoon Hardware-in-the-Loop (HIL) platform using real measured load and PV production data. A rule-based energy management system (EMS) is proposed and its effective-ness is analyzed through testing resilience of the system under consideration. Performance analysis is conducted by adding an EV and assessing system response in several scenarios of load and EV use profiles. Through Typhoon HIL simulations the power profiles of system elements are analyzed, leading to conclusions on BIM performance.

Building integrated microgrids and building integrated photovoltaic systems (BIPV) are emerging as a promising avenue for seamlessly integrating small scale renewable energy sources (RES) into the grid. Challenges arise as new ideas are being explored and implemented in this area, and one of them is maximizing self-consumption and self-sufficiency, for any energy policy, but especially while adhering to zero energy export (ZEE) policy restrictions. As a solution to enhance the utilization of BIPV system this paper proposes a load management (LM) technique. By combining on-grid photovoltaic (PV) system with controllable loads, this paper demonstrates how proactive LM can increase self-consumption and self-sufficiency factors, as well as mitigate PV produced energy dumping due to ZEE restrictions. A case study in the wood sector's industrial building illustrates the efficiency of this approach, showcasing reduced reliance on grid power during sunny periods and increased self-sufficiency through strategic load scheduling. Real-world data analysis validates the effectiveness of LM in aligning PV generation with building energy demands, offering insights into its potential for broader adoption in the renewable energy sector.

A fast-paced policy context is characteristic of energy and climate research, which strives to develop solutions to wicked problems such as climate change. Funding agencies in the European Union recognize the importance of linking research and policy in climate and energy research. This calls for an increased understanding of how stakeholder engagement can effectively be used to co-design research questions that include stakeholders'concerns. This paper reviews the current literature on stakeholder engagement, from which we create a set of criteria. These are used to critically assess recent and relevant papers on stakeholder engagement in climate and energy projects. We obtained the papers from a scoping review of stakeholder engagement through workshops in EU climate and energy research. With insights from the literature and current EU climate and energy projects, we developed a workshop programme for stakeholder engagement. This programme was applied to the European Climate and Energy Modelling Forum project, aiming to co-design the most pressing and urgent research questions according to European stakeholders. The outcomes include 82 co-designed and ranked research questions for nine specific climate and energy research themes. Findings from the scoping review indicate that papers rarely define the term'stakeholder'. Additionally, the concepts of co-creation, co-design, and co-production are used interchangeably and often without definition. We propose that workshop planners use stakeholder identification and selection methods from the broader stakeholder engagement literature.

The International Transport Forum (ITF) predicts a significant increase in demand for transportation in the coming years, despite the shortage of drivers. To tackle this challenge, the Transport and Logistics (T&L) industry is increasingly relying on emerging technologies. While connected and autonomous driving offer promises of greater safety, efficiency, and environmental benefits, connected and autonomous driving face operational hurdles in complex environments. However, the existing limitations of autonomous vehicles, particularly in dense urban settings, highlight the need for complementary technologies, such as teleoperation. The European Horizon 2020 5G-Blueprint project aims to design and validate the technical architecture and business models for cross-border teleoperated transport, utilizing 5G technology. This study delves into the implementation of a real 5G Standalone (5G SA) network within a port environment, utilizing network slicing for teleoperation and Multi-Access Edge Computing (MEC) to enable real-time video processing at the network edge. Specifically focusing on Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) slices, we conduct a comprehensive evaluation of a real-world 5G SA network. Our assessment examines key performance parameters such as Round-Trip Time(RTT) latency, Packet Delivery Rate (PDR), Reference Signals Received Power (RSRP), and corrupted frame rates, emphasizing the crucial role of 5G network slicing and MEC in enhancing operational reliability and efficiency in teleoperated transport systems.

The Transport and Logistics (T&L) sector faces numerous challenges, including the search for qualified personnel, as well as improving driver safety and work-life balance. Teleoperation emerges as the technology able to address these challenges. Thanks to 5G connectivity and network slicing, operating vehicles remotely from a Teleoperation Center (ToC) is becoming a reality. The European project 5G-Blueprint, funded by the European Union, has demonstrated the feasibility of 5G-based teleoperation, even in a cross-border context. Despite the fact that 5G and network slicing enable reliable and low-latency transmission of video data from cameras installed on Teleoperated Vehicles (ToVs) to ToC, the perception of the surrounding environment is different for the teleoperator compared to the driver who is physically present in the vehicle. In this paper, we introduce a real-world system that showcases synergy among different teleoperation elements, including intelligent traffic lights (iTL) and Vulnerable Road Users (VRU), aimed at supporting teleoperation by improving remote driver’s situational awareness. This synergy enhances the environmental perception of the teleoperator, bridging the gap between their experience and that of an in-vehicle driver. First, we evaluate the performance of a real-world 5G network with network slicing, based on actual data and testing scenarios conducted in both industrial and urban areas with 5G Standalone (5G SA) coverage. Then we validate the 5G capabilities for enabling a real-world system that showcases synergy among different teleoperation elements.

The native integration of AI and ML algorithms in the next-generation mobile network architecture will allow for meeting the expectations of 6G. This aspect is targeted by the DAEMON project, which proposed a solution to natively manage Network Intelligence (NI) through novel architectural elements and procedures. In this paper, we discuss how NI solutions based on AI and ML can leverage NI native procedures implemented by the NI Orchestrator to improve their lifecycle management. We also discuss how the architectural procedures can be implemented in practice, using state-of-the-art software components.

Intent-driven network management has become an important part of autonomous systems in Beyond 5G (B5G) towards Sixth-Generation (6G) networks, by enabling flexibility in the interaction among applications, operators and users. Intents play an important role in the communication of road users like autonomous vehicles and pedestrians to edge computing services. As sensor technologies for modern vehicles are cheaper, smaller, diverse and computing capable, more demand for applications and services on the road is increasing. A flexible intent interpretation and coordination are needed to deal with the dynamic environment and constantly changing goals. This paper presents a proof-of-concept of Zero-touch Network and Service Management (ZSM) for vehicular communication services, using an Intent Management Entity (IME) to translate user objectives into actionable directives. This paper describes a realistic testbed setup at the Smart Highway, where a Deep Reinforcement Learning (DRL) algorithm is used to optimize the selection of Roadside Units (RSUs) for service orchestration. This paper also discusses the challenges and opportunities of enhancing the IME with time-based intent coordination, using Artificial Intelligence and Machine Learning (AI/ML) techniques to estimate the waiting time and priority in intent coordination. The paper aims to demonstrate the benefits of ZSM and Intent-driven Management for vehicular edge computing and B5G/6G autonomous network management frameworks.

Network slicing enables multiple virtual networks to share physical resources, allowing network operators to deliver highly customizable and efficient networking solutions that meet the diverse requirements of modern applications. The automated management of network slices has been studied in the last years to make such solutions more flexible, ready to support new applications, and capable of optimizing network resource utilization. Many works in the literature give a top-down approach, focusing on the high-level decision processes, and relying on abstracted infrastructure managers and simulation tools to apply/execute such decisions. In this work, we leverage components that we previously developed for network monitoring, flexible traffic shaping, and Software-Defined Time-Sensitive Networking control, to create a bottom-up approach toward automated slice management. We describe the intricate coordination of elements required for an automated control loop and present the results achieved with a proof-of-concept executed in a real testbed of wired and Wi-Fi nodes. The results show the capability of the system to correctly identify the bottleneck of a flow and apply corrective actions to reestablish its intended performance level.