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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.

Despite successful AI system deployments in manufacturing, methodological support for developing and integrating AI systems into manufacturing processes remains underdeveloped. This paper aims to identify gaps in the methodological support for the early design phase of AI system development in manufacturing. The study reveals the thinking-level challenges that design participants face in the early design phase and identifies remedies for those challenges, which are only superficially addressed in current manufacturing literature. The paper contributes to uncovering the current knowledge gap in developing an actionable methodology for AI system development in manufacturing contexts.

An increasing number of manufacturing companies have initiated designing and implementing AI systems in manufacturing, however, with limited success. Within our overarching research objective of establishing a methodology for the development of AI systems in manufacturing with socio-technical system consideration, this paper focuses on the early design phase of the development life cycle and aims to identify factors that are essential in the phase but whose importance has been less addressed in the manufacturing literature. To this aim, a case study was conducted adopting a design science approach. The case company was developing an ML-based anomaly detection system for a casting process. The researcher organised an AI system design workshop where participants from the company used the Human-AI design guidelines created by a leading large software company. The workshop enabled the participants to explore a wide range of design concerns. It, however, caused the confusing experience that they had to deal with too many questions simultaneously without clear guidance. Analysing this negative experience has led to identifying four design issues requiring further attention in the research. An example of these issues is that the interdependency of design decisions on operational procedures, human-machine interfaces, ML models, pre-processing, and input data makes it challenging to design these elements in isolation. The study found that a structured approach to dealing with the identified issues was currently lacking. This paper contributes to the manufacturing research community by addressing key unresolved issues in the research through highlighting practical details of designing AI systems in manufacturing.

The transition towards sustainable electrification, particularly in the context of electric vehicles (EVs), necessitates a comprehensive understanding and effective management of battery circularity. With a plethora of EV models and battery variants, navigating the complexities of circularity becomes increasingly challenging. Furthermore, efficient fleet management emphasizes the necessity for robust data collection and analysis across diverse EVs to optimize battery value throughout its lifecycle. Advanced digital technologies play a crucial role in bridging informational gaps and enabling real-time connectivity, intelligence, and analytical capabilities for batteries. However, despite the potential benefits, the integration of circularity and digital technologies in the battery sector remains largely unexplored. Both circularity and digital technologies in the battery domain are still emerging, lacking conceptualization on their integration. To tackle these challenges, this paper advocates for the concept of smart battery circularity, which amalgamates advanced digital technologies with circular economy principles. The purpose of this paper is to enhance the conceptualization of smart battery circularity and elucidate the key knowledge areas necessary to facilitate it. The study identifies three critical knowledge areas essential for enabling smart battery circularity: digitally enabled circular business models, digital twin platforms for circular battery services, and smart battery performance monitoring. The sub-areas within each key knowledge area are also outlined. By delineating these knowledge areas, the study proposes an integrative framework, showcasing how these areas contribute to smart battery circularity both individually and collectively. The study offers insights to accelerate the development of initiatives aimed at establishing a sustainable and circular battery ecosystem, thereby advancing global efforts towards climate-neutral electrification. 

This paper presents a collaborative solution developed to enable people without prior Internet of Things (IoT) knowledge to ideate, conceptualise, role-play and prototype potential improvements to their work processes and environments. The solution, called the Karakuri IoT toolkit and method, was tested in two workshops with eight production leaders at a Swedish manufacturing company. Outcomes were analysed from the perspectives of materials interaction and instruments of inquiry. Results indicate the solution can help people conceive and prototype improvement ideas at early design stages.

Artificial Intelligence offers a wide variety of capabilities that can potentially address people's needs and desires in their specific contexts. This pilot study presents a collaborative method using a deck of AI cards tested with 58 production, AI, and information science students, and experts from an accessible media agency. The results suggest that, with the support of the method and AI cards, participants can ideate and reach conceptual AI solutions. Such conceptualisations can contribute to a more inclusive integration of AI solutions in society.

Embedded systems are today often self-sufficient systems with limited communication. However, this traditional view of an embedded system is changing rapidly. Embedded systems are nowadays evolving, e.g., an evolution pushed by the increased functional gain introduced with the concept of System of Systems (SoS) that is connecting multiple subsystems to achieve a combined functionality and/or information of a higher value. In such a SoS the subsystems will have to serve a dual purpose in a) the initial purpose that the subsystem was originally designed and deployed for, e.g., control and protection of the physical assets of a critical infrastructure system that could be up and running for 30-40 years, and b) at the same time provide information to a higher-level system for a potential future increase of system functionality as technology matures and/or new opportunities are provided by, e.g., greater analytics capabilities. In this paper, within the context of a “dual purpose use” of a) and b), we bring up three central challenges related to i) information gathering, ii) life-cycle management, and iii) data governance, and we propose directions for solutions to these challenges that need to be evaluated already at design time.

The increasing functionality and complexity of automotive applications requires not only the use of more powerful hardware, e.g., multi-core processors, but also efficient methods and tools to support design decisions. Component-based software engineering proved to be a promising solution for managing software complexity and allowing for reuse. However, there are several challenges inherent in the intersection of resource efficiency and predictability of multi-core processors when it comes to running component-based embedded software. In this paper, we present a software design framework addressing these challenges. The framework includes both mapping of software components onto executable tasks, and the partitioning of the generated task set onto the cores of a multi-core processor. This paper aims at enhancing resource efficiency by optimizing the software design with respect to: 1) the inter-software-components communication cost, 2) the cost of synchronization among dependent transactions of software components, and 3) the interaction of software components with the basic software services. An engine management system, one of the most complex automotive sub-systems, is considered as a use case, and the experimental results show a reduction of up to 11.2% total CPU usage on aquad-core processor, in comparison with the common framework in the literature. 

Although scholars and practitioners are actively discussing the potential benefits of introducing Internet of Thing (IoT) in production, IoT is still as an expensive solution in terms of investment and high technological threshold. Manufacturing companies seek a simpler and lower-cost approach to adopting IoT technologies in production, allowing companies to take advantage of the knowledge and innovation capabilities of people close to shop floor operations. This paper introduces the concept of “Karakuri IoT” – simple and low-cost IoT-aided improvements driven by the people close to shop floor operations. A pre-study is conducted to examine the feasibility of the concept. This paper presents the results of the pre-study.

This paper investigates the interplay of cloud computing, fog computing, and Internet of Things (IoT) in control applications targeting the automation industry. In this context, a prototype is developed to explore the use of IoT devices that communicate with a cloud-based controller, i.e., the controller is offloaded to cloud or fog. Several experiments are performed to investigate the consequences of having a cloud server between the end device and the controller. The experiments are performed while considering arbitrary jitter and delays, i.e., they can be smaller than, equal to, or greater than the sampling period. This paper also applies mitigation mechanisms to deal with the delays and jitter that are caused by the networks when the controller is offloaded to the fog or cloud.