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The rapid growth of the electric vehicle (EV) industry has significantly increased the demand for EV batteries, raising concerns about their environmental impact and end-of-life management. EV batteries face several challenges throughout their lifespan, including performance degradation, limited usability during their first life, and complex recycling processes. Addressing these issues requires a fundamental shift toward circular economy (CE) principles. This study examines how circular strategies—particularly those outlined in the 10R framework: Refuse, Rethink, Reduce, Reuse, Repair, Refurbish, Remanufacture, Repurpose, Recycle, and Recover—can be applied across the EV battery lifecycle to enhance value retention. Drawing on semi-structured interviews with key actors in the EV battery ecosystem—such as battery manufacturer, EV manufacturer, EV operator, battery recycler, and technology providers—this study investigates how 10R strategies are interpreted and implemented in practice and explores the role of advanced digital technologies in supporting these efforts. The results show that actors are currently adopting and planning to adopt diverse 10R strategies to promote a CE for EV batteries. All major digital technologies associated with Industry 4.0 are being applied, with AI, blockchain, IoT, big data analytics, cloud technology, simulation, and digital twins being among the most widely used. This study provides exploratory insights into the role of digital technologies in implementing circular strategies in the EV battery circularity sector, which helps understand how these technologies and collaboration can close the loop.

Digital twins, dynamic digital representations of physical systems, are emerging as transformative tools for enhancing crisis preparedness and resilience in critical societal sectors. By enabling real-time monitoring, simulation, and optimization, these technologies offer actionable insights to support proactive risk mitigation, efficient resource allocation, and continuous improvement of crisis response strategies. This study provides a comprehensive knowledge overview of digital twins, focusing on their applicability and impact in key sectors such as energy, healthcare, and transportation. Specifically, it examines the essential services most suited for digital twin adoption, the role of safety-critical data throughout their life-cycle, and their utility in identifying and mitigating risks within critical infrastructure. We employed a mixed-methods research design, combining systematic and gray literature reviews with expert interviews to integrate academic insights with practical perspectives. The findings reveal significant opportunities for digital twins to enhance operational efficiency, strategic planning, and crisis management. However, practical implementation remains in its infancy, with challenges related to cost, complexity, and limited real-world applications. In addition, this study provides actionable recommendations for stakeholders, emphasizing investment in digital twin technologies, robust data governance, and the development of standardized protocols. Future research directions include exploring applications of DTs in emerging sectors, such as crisis preparedness and societal resilience, advancing artificial intelligence integration, and adopting a system-of-systems perspective to address societal challenges comprehensively.

Context: Digital twins serve as virtual representations of systems, enabling capabilities such as intelligent monitoring, real-time control, decision-making, and predictive analytics. The Asset Administration Shell (AAS) is the pivotal Industry 4.0 standard for digital twin engineering. In parallel, the Systems Modeling Language (SysML) has emerged as a modeling standard for systems engineering, providing a formalized and semantically rich approach to system modeling. SysML v2 is its recent evolution. With its growing adoption, multiple models are expected to be widely available, each capturing different facets of the modeled system by leveraging diverse engineering capabilities offered by various tool ecosystems. Problem: Instead of manually re-creating models for digital twinning, existing system models should be leveraged to relieve repetitive modeling tasks. While SysML v2 and AAS are prominent standards in DT engineering, they lack direct integration, necessitating a dedicated approach for their seamless interoperability. Purpose: This paper presents a practical investigation into the conceptual alignment between the SysML v2 and AAS specifications, with a focus on their structural and behavioral modeling aspects. It proposes an implementable approach for mapping SysML v2 to AAS, enabling the automated generation of AAS models from SysML v2 models. Method: To realize this approach, we employ model-driven engineering techniques leveraging the Eclipse Modeling Framework (EMF) and model transformations based on the Query View Transformation (QVT) language. The proposed model transformation incorporates query mechanisms for extracting structured elements, preserving information and structural integrity, and ensuring static semantic consistency at design-time and seamless integration between the two investigated standards. We develop and validate the model transformation following an iterative test-driven development approach using an existing set of 24 SysML v2 examples, sourced from the official SysML v2 repository. Result: We deliver a QVT-based, EMF-compliant transformation that automatically generates AAS submodel templates from SysML v2 models, preserving structural hierarchies and behavioral semantics via dedicated AAS concepts and their extension. Through an iterative, test-driven development process, we validate metamodel conformance, information preservation, and structural integrity. The current mapping addresses design-time concepts, and the implementation supports forward transformation. All conceptual mappings, QVT scripts, and example artifacts are publicly available in a dedicated repository.

With the rapid development of the electric vehicle (EV) industry, the large-scale retirement of batteries has introduced multiple environmental risks and challenges to industrial sustainability. Smart circular business models (SCBMs) utilizing 10R circular strategies (i.e., Refuse, Rethink, Reduce, Reuse, Repair, Refurbish, Remanufacture, Repurpose, Recycle, and Recover) present a promising solution. However, the success of implementing such strategies, policies and regulations is considered the core support factor. This study combines a review of regulatory frameworks with semi-structured interviews in the Swedish EV battery ecosystem to explore the key challenges in implementing the 10R strategies under the Battery regulation. Finally, it identified four interrelated pillars that shaped battery circularity: (1) policies and regulations, (2) digital technologies, (3) business models and economics, and (4) organizational and behavioral management. These pillars illustrate how various dimensions intersect to influence the implementation of 10R strategies for EV batteries. This study contributes to both theory and practice by offering a conceptual policy pathway for translating the 10R strategies to actionable strategies. It provides guidance for Europe policymakers and industry actors to build a digitally empowered, regulations-supported, and standardized circular battery ecosystem.

With the rapid development of the electric vehicle (EV) industry, the large-scale retirement of batteries poses significant environmental risks and challenges to industrial sustainability. Circular business models based on the 10R circular strategies offer a promising solution. However, their successful implementation largely depends on policies and regulations, which serve as th ecore enabling factor. This study combines a review of regulatory frameworks with semi-structured interviews in the Swedish EV battery ecosystem to explore the interrelated pillars and policy pathways shaping the implementation of the 10R strategies under the Battery Regulation. The analysis identifies four interrelated pillars: policies and regulations, digital technologies, business models and economics, and organizational and behavioral management. These pillars demonstrate how different dimensions intersect to influence the adoption of 10R strategies for EV batteries. The study contributes to both theory and practice by proposing a conceptual policy pathway for translating the 10Rs into actionable strategies. It provides guidance for policymakers and industry actors in building a digitally enabled, regulation-supported, and standardized circular battery ecosystem.

This paper presents a prototype AI-powered decision support system that leverages large language models (LLMs) and digital agents to assist furnace-stage operations in transformer manufacturing. The system integrates real furnace sensor data with synthetic manuals and expert insights using a modular architecture that combines Retrieval-Augmented Generation (RAG), structured prompting, multi-agent coordination, and response validation. It processes natural language queries to generate context-aware responses grounded in process data and documentation, demonstrating the potential of domain-adapted generative AI for industrial support. Experiments show a 100% improvement over a baseline LLM, though performance remains 29% below ChatGPT-4o, indicating both promise and areas for future improvement.

In this article, we present a machine-learning approach to predict the execution time of concurrent applications by leveraging isolated execution times and performance monitoring counters. Through extensive experiments across various application pairs, we explore the challenges of modeling execution time in a concurrent environment. This study highlights three progressively refined models, transitioning from simple neural networks to complex Bayesian-optimized ensemble techniques. A key finding highlights the significant role of interference in execution time variability as shared resource contention leads to deviations from isolated behavior. These insights enhance the understanding of the role of scheduler and application dynamics in concurrent environments. 

Resource sharing in multicore architectures remains one of the major challenges limiting the potential performance benefits of such systems, particularly in industrial domains that demand high performance and strict timing guarantees. In this paper, we propose a novel synchronization protocol, called Multi-replicas Time-bounded Consistency (MTC), designed to mitigate the impact of resource sharing among tasks located on different cores. The MTC protocol replicates shared resources while ensuring a bounded level of consistency among the replicas within a defined time constraint. We present a response-time analysis for the proposed solution and demonstrate that MTC can significantly simplify response-time analysis compared to traditional lock-based synchronization methods, potentially offering a more efficient and scalable alternative.

Transitioning to a circular economy for electric vehicle (EV) batteries requires both systemic circular business models (CBMs) innovation and practical implementation, where the 10R strategies (refuse,rethink, reduce, reuse, repair, refurbish, remanufacture, reuse, recycle, and recover) provide more operational approaches within the battery lifecycle. Smart CBMs integrate digital technologies intoCBMs to enable real-time data use, automation, and optimization across the battery lifecycle.However, the understanding of how these technologies support CBMs, particularly regarding the 10R, lacks conceptual integration at strategic and operational levels. Hence, the study contributes to map the literature on smart circular business strategies for EV batteries and identifies five key dimensions for smart CBMs: digital technologies, battery ecosystem actors, service types and KPIs, policies, barriers and enablers, and 10R strategies. A conceptual framework is then proposed to illustrate how the interconnections support smart CBM development in the EV battery ecosystem.

As battery technologies become central to the global energy transition, optimizing their performance during first-life use is essential for maximizing value and enabling circular economy pathways. First-life electric vehicle (EV) battery operations—including deployment, usage, maintenance, and early-stage diagnostics—are increasingly influenced by advanced digital technologies, data management practices, and artificial intelligence (AI). Despite rapid technological advances, significant research and implementation gaps remain in integrating data-driven approaches and AI models into operational decision-making and lifecycle optimization. This paper addresses these challenges through an exploratory qualitative study, drawing insights from three expert workshops involving battery ecosystem actors. Our analysis identifies four key thematic areas: (1) battery lifecycle optimization, (2) risk and responsibility distribution, (3) data ownership and interoperability, and (4) AI deployment and cybersecurity. The findings highlight tensions between short-term operational cost-efficiency and long-term battery health, the fragmentation of risk management responsibilities, and growing concerns around data sovereignty and AI system integrity. Based on these insights, we propose a guiding framework for smarter first-life EV battery operations, structured around four pillars and supported by four cross-cutting enablers. This study contributes to the emerging discourse on battery circularity by advancing the understanding of strategies for smarter first-life battery operations.