Högskolan i Skövde

The transition to a low-carbon economy is a critical global challenge. However, the relative effectiveness of renewable portfolio standards (RPS) and carbon tax policies, particularly their potential synergistic effects, remains insufficiently understood. Clarifying this relationship is essential for designing effective policies that enhance renewable energy investment and reduce carbon emissions. We develop an optimization framework in which a monopolistic electricity supplier in China invests in a hybrid energy system of conventional and renewable power. We compare profit-maximizing strategies under four policy scenarios: no policy, a carbon tax, an RPS, and a mixed policy (carbon tax + RPS). The analysis examines how these policies shape production and pricing decisions, investment, emissions, consumer surplus, and social welfare. Three main findings emerge. First, both carbon tax and RPS policies significantly encourage renewable energy investment and reduce carbon emissions. Second, mixed policies consistently outperform single policies, demonstrating the highest potential for renewable energy development and carbon emission reduction. Third, social welfare effects under mixed policies vary depending on the carbon tax and renewable energy quota level, with the highest social welfare achieved under low and medium quotas. Importantly, renewable intermittency does not alter the relative ranking of policies, though consumer environmental awareness shifts investment thresholds. Based on these findings, three recommendations emerge: integrating carbon tax with RPS accelerates renewable deployment, phased emission reduction balances neutrality targets with electricity demand, and incentives linking green electricity with consumer awareness strengthen adoption. These results provide actionable policy guidance for China and offer broader insights for economies pursuing a sustainable low-carbon transition.

Given the eruption of AI technology, the cooling requirement in data centres has drawn significant attention due to the increasing demand for data processing and storage. Indirect evaporative cooling (IEC) is a cutting-edge cooling technology with huge advantages of energy economy and environmental friendliness compared with conventional mechanical vapour compression cooling systems. Herein, we perform a levelized cost analysis (LCA) to determine the economic performance and energy consumption of the traditional mechanical vapor compression (MVC) cooling system and a novel hybrid IEC + MVC cooling system in data centre applications. A data centre model is adopted and applied in various climate zones in 10 cities from 8 countries. The results showed that the hybrid IEC + MVC system presented energy savings in all the cities, especially in Riyadh, with an energy saving of 41.3 GWh for the year. Most cities showed cost saving with the hybrid system, with London and Madrid achieving cost savings of 52–53%. All the cities showed significant CO2 reduction with the hybrid system, especially in Riyadh (23 547 tons), Jeddah (18 740 tons), and Dubai (12 432 tons). This study sheds light on the cost analysis based on levelized cost analysis (LCA) for next-generation cooling technology for data centres.

The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework. Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and influence the RDT system based on this architecture. Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels.

This review examines 74 original articles published between 2023 and partially 2026 in the Scopus database, focusing on how sustainability indicators are conceptualised and applied to support circular economy transitions in the water sector. Recent literature shows a rapidly expanding suite of assessment tools, ranging from life cycle and circularity metrics to indicators of water productivity, resource recovery potential, wastewater reuse efficiency and governance-oriented frameworks embedded in Water-Energy-Food-Environment nexus approaches. Emerging trends point toward increasingly multidimensional and integrated indicator systems, yet ongoing challenges, such as energy-circularity trade-offs, fragmented regulatory environments and difficulties in adapting indicators to diverse socio-economic and ecological contexts, limit wider implementation. The review highlights priority directions for advancing the field, including the development of harmonised yet flexible indicator sets, stronger alignment across governance levels, and the wider adoption of digital monitoring tools to enable real-time assessment and evidence-based decision-making in circular water systems. 

Predicting part machining deformation is vital for optimizing design and manufacturing processes, thereby enhancing thequality and performance of heavy machinery parts. Traditional numerical methods, such as the finite element method, arelimited by their computational inefficiency. Furthermore, recent data-driven approaches for predicting machining deforma-tion face challenges due to the complex features and variable geometries of parts throughout design iterations and machin-ing processes. To this end, this paper proposes a method, Voxel-FNO, which rapidly predicts machining deformation forparts with variable feature geometry. This method utilizes the Fourier neural operator to capture the underlying mechanisticrelationship between residual stress and machining deformation of parts. Both stress and geometry are sampled by voxelinto standard domain before being input into the neural network model. This approach ensures efficiency and applicability,even as part geometries change. The proposed method is verified in both simulation and real environment, demonstrating itsaccuracy, stability, and generalization capability for varying part geometries, compared to the accurate results from the finiteelement method. It shows prediction max errors of 0.003 mm, 0.002 mm, and 0.018 mm, and RMSE of 0.0003 mm, 0.0002mm, and 0.0013 mm for deformations in X, Y, and Z directions, respectively, compared with FEM results.

The recycling of end-of-life (EOL) products poses significant challenges due to inefficient and unsafe disassembly processes. To address this, we propose a novel self-perception human-robot collaboration (HRC) system that enhances disassembly efficiency and safety through multi-modal human intention recognition. Our core methodological innovation lies in the real-time fusion of three key perception modules: action recognition using a Spatial-Temporal Graph Convolutional Network (ST-GCN), disassembly tool detection based on an enhanced YOLO algorithm, and facial angle recognition for operator awareness inference. A dedicated dataset for retired power battery disassembly was constructed to support this research, encompassing human skeletal data for action recognition, labeled images for tool detection, and facial expression detection. The proposed system was validated on a physical HRC disassembly platform. Experimental results demonstrate a marked improvement, with our integrated intention recognition method achieving an accuracy of approximately 85 %, significantly outperforming traditional single-modality approaches. Furthermore, the HRC disassembly operation was completed in 238 s, which is 60 s (or 20 %) faster than purely manual disassembly. This work provides a robust and efficient HRC disassembly framework for intelligent disassembly scenario understanding, contributing to advancing circular manufacturing. 

The formulation and selection of parameters and sequences in crankshaft production present challenges that are both demanding and time-intensive. This study introduces an innovative approach to intelligent process planning in crankshaft machining lines using multi-turret machines. Emphasis is placed on automating process planning through multi-objective optimization of critical decisions such as process parameters, operation sequencing, and tool positioning on turret magazines. The principal objectives addressed include minimizing machining and non-machining time, reducing costs by optimizing tool life, and enhancing product quality through optimal surface roughness. By automating these decision points, the proposed framework reduces manual intervention and aligns with Industry 4.0 goals for adaptive, data-driven manufacturing. Additionally, we discuss the potential future incorporation of artificial intelligence agents to dynamically refine parameters and enable adaptive planning.

Laser welding of busbars to battery tabs in electric vehicles (EVs) is crucial due to the rapid advancements in electric mobility technology. Ensuring weld quality is paramount, as it depends on factors such as porosity generation, fluid flow in the molten pool during welding, applied laser power, and welding speed. However, conventional laser welding techniques, which primarily focus on adjusting laser parameters along the weld direction, struggle to effectively mitigate porosity formation. While the effect of laser angles along the weld direction has been extensively studied, the effects of off-axis laser angles, i.e., angled in the plane perpendicular to the weld direction, have not yet been explored. This study introduces an innovative approach to laser welding by varying the laser off-axis angle at different laser energy densities to optimize the process specifically for porosity reduction. By implementing a three-dimensional computational fluid dynamics (CFD) model of laser welding of aluminum AA1050, we provide a detailed analysis of the fluid flow and melt pool dimensions while employing different off-axis angles. Our model incorporates multiple reflections, upward vapor pressure, and recoil pressure to explain porosity formation at different laser off-axis angles. The results show that increasing the laser off-axis angle at optimized laser power and welding speed significantly reduces porosity. The numerical analysis indicates a maximum deviation from the experimental melt pool width of 11% at a laser off-axis angle of 4.92° and a minimum error of 2.6% at an off-axis angle of 2.74°. For melt pool depth, the maximum deviation is 7.2% at an off-axis angle of 4.92°, and the minimum difference is 0.5% at an off-axis angle of 7.42°. This study presents a novel methodology for improving laser welding processes by addressing the specific challenge of porosity formation.

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system. 

For battery pack assemblies, it is crucial that the laser welded cell-to-busbar joints demonstrate both high mechanical strength and minimal electrical resistance. The present study investigates the effect of different laser welding parameters, on the mechanical strength, electrical resistance, porosity formation and joint microstructure, for dissimilar material cell-to-busbar joints. Laser welding experiments are performed, on thin nickel-plated copper and steel plates. The plates are joined in an overlap configuration, using laser beam wobbling and power modulation. Both circular and sinusoidal laser beam wobbling are used as selected strategies to increase the interface width of the joints, where also a comparison is made between the two methods. The joint quality is evaluated using joint geometry analysis, shear strength tests, computed tomography scanning and electrical resistance measurements. The results show that circular laser beam wobbling gives a larger joint shear strength compared with sinusoidal laser beam wobbling. In addition, it is observed that both the total pore volume and material mixing are significantly increased with increasing laser power and wobbling frequency for circular laser beam wobbling. However, for the sinusoidal laser beam wobbling the wobbling frequency does not show a significant impact on the total pore volume.