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Senast publicerade

Planering och styrning av en flotta autonoma mobila robotar med centraliserad rörelsespårning
(2026) von Brömsen, Julia; Edofsson, Elsa; Leffler, Tim; Lindblom, Oscar; Rajabi, Altaf; Strandberg, Debora
Autonomous mobile robots are increasingly utilized in logistical workflows and material flow management. The process of automating heavy transportations leads to increased efficiency of material handling while simultaneously decreasing manual labour. This study investigates the coordination of a scalable fleet of autonomous mobile robots (AMR) as they transition from logistical patterns to dynamic scenarios. External real time data about the location of a moving target is obtained from a drone based surveillance system whereas the fleet is traced in real time by a motion capture system with high precision localization. The realisation of the system is achieved through a strategy of efficient path planning and collision avoidance. An A* path planning strategy A* is implemeted which utilizes euclidean distance heuristics to minimize the cost between start and goal positions. This determines the optimal trajectory from inital, to designated position. As the fleet scales, the increasing number of agents leads to collisions and conflicts such as deadlocks. This issue is addressed through a multi agent path finding (MAPF) strategy evaluating a local collision resolver A* (LCRA*). LCRA* is based on the model lifelong priority based search (LPBS) and the benefit of using LCRA* is that it significantly improves computational performance as it does not require global time optimization to handle collisions locally. This results in a smooth transition between a logistical pattern mode or an intruder mode in order to collectively encircle the moving target.
Looking Beyond Marginal Pricing of Electricity Reimagining Market Design for a Near-Zero Marginal Cost Electricity System
(2026) Brechter, Erik; Eliasson, Adam
The Nordic power market is organized around an energy-only design in which marginal pricing of electricity and the merit order coordinate dispatch and, in principle, remunerate investment. As the generation mix shifts toward near-zero marginal cost sources, primarily variable renewables, hydropower and nuclear, the price signal that this design relies on becomes structurally weaker, while capitalintensive technologies depend on revenue predictability scarcity rents alone do not provide. This thesis examines how the transition toward near-zero marginal cost generation of electricity affects price formation, investment incentives and system adequacy in the Nordic context. It evaluates structurally distinct market design alternatives against the stated challenges. The study adopts a qualitative, abductive research design combining an integrative literature review with two rounds of semi-structured expert interviews. Four market designs are constructed and positioned along a spectrum of intervention: the energy-only status quo, a Mosaic model that layers complementary mechanisms onto the existing architecture, a Broadband model that anchors revenue in contracted capacity rather than delivered electricity, and a Command model based on centralized state planning. Each design is assessed against a five-criterion framework covering price formation efficiency, investment adequacy, system adequacy and flexibility incentives, regulatory and political feasibility, and stakeholder acceptance. The analysis finds that the Nordic clearing price currently remains functional largely due to hydropower’s water value mechanism and European market coupling rather than marginal pricing, and that scarcity rents tend towards becoming insufficient in order to incentivize long-term investment. No design dominates across all criteria, but the Mosaic model emerges as the most empirically supported and institutionally feasible direction. The Broadband and Command models address the underlying coordination problem, but at costs the Nordic and EU context cannot currently absorb. The central contribution of the thesis is to reframe the design question, rather than choosing between distinct market architectures. The more productive framing is which combination of complementary mechanisms best addresses the specific structural challenges the Nordic system faces at each stage of the transition.
Optical Oscillator Evaluation in Radio Test Bed - Integration and Phase Noise Characterization of a Microresonator-Based Optical Oscillator
(2026) Hiselius, David
Local oscillator (LO) phase noise presents significant challenges in achieving high capacity wireless communication at millimeter-Wave frequencies. Through optical frequency division (OFD), high-Q resonators may be leveraged to generate low phase noise microwave frequency signals. In this work, Kerr microresonator-based OFD was integrated as an LO source in a point-to-point link radio test bed. The frequency divider and amplifier used to facilitate integration of the optical oscillator into the radio test bed were characterized from a phase noise perspective. The optical os cillator was subsequently analyzed by correlating observations in the phase noise spectrum to known noise mechanisms. Finally, radio link performance for external local oscillator sources, including the optical oscillator under study, was compared to an unmodified channel. Results indicate that photodetection noise constituted the primary performance limitation of the optical oscillator implementation, resulting in a comparatively high phase noise floor (-145 dBc/Hz). The impact of noise intro duced by the frequency divider and amplifier on radio link performance is estimated to be minor; however, residual phase noise measurements are required to verify this assumption. In radio link measurements, the optical oscillator achieved signal-to noise ratios approximately 6 dB lower than those of the unmodified channel. The observed degradation in radio link performance is consistent with previous studies identifying the LO noise floor as a dominant limitation in wideband communication systems. As the oscillator demonstrated excellent near-carrier phase noise (-130 dBc/Hz @ 100 kHz) and established methods of photodetection noise mitigation exist, further investigation is warranted.
Physics-Informed Machine Learning model for Emission Prediction in Exhaust Aftertreatment Systems: Prediction of NOX, CO, HC and NH3 Emissions for Volvo Penta Off-Road Diesel Engines
(2026) Persson, Linnea
Modern industrial diesel engines rely on advanced exhaust aftertreatment systems (EATS) to comply with increasingly stringent emission regulations such as EU Stage V, reducing emissions of nitrogen oxides (NOx), hydrocarbons (HC), carbon monox ide (CO), and ammonia slip (NH3). Among these systems, selective catalytic re duction (SCR) plays a central role in NOx reduction through urea-based ammonia dosing. However, SCR development, calibration, and catalyst sizing still depend heavily on expensive and time-consuming physical engine and rig testing. This cre ates a strong industrial need for reliable predictive models that can complement physical testing through early-stage virtual evaluation. This thesis investigates the use of physics-informed feature engineering combined with machine learning for predicting system-out emissions in Volvo Penta off-road diesel engine platforms. The objective is to develop a supervised regression frame work capable of predicting system-out emissions based on engine-out conditions and exhaust aftertreatment parameters, while improving robustness and physical inter pretability compared to purely data-driven approaches. Particular focus is placed on NOx and NH3 prediction, since these emissions are most strongly linked to SCR behaviour and catalyst dynamics. The study uses existing experimental test data from Volvo Penta engines in the D5 D13 platform range, representing industrial and off-road applications. Separate XG Boost regression models were developed for each target variable using both directly measured signals and derived physics-informed features related to SCR behaviour, catalyst thermal conditions, flow dynamics, and ammonia availability. Model per formance was evaluated using ShuffleSplit cross-validation together with validation on unseen datasets, primarily using the coefficient of determination (R2) and Mean Absolute Error (MAE). The results show that physics-informed feature engineering improved both model ac curacy and robustness compared to baseline models using only directly measured in puts. The strongest improvements were observed for NOx prediction, where physics informed features improved model accuracy and robustness across different operating conditions and engine platforms. The final models also achieved strong predictive performance for HC and CO, while NH3 prediction remained more challenging due to the complexity of ammonia storage and slip behavior. Feature importance analy sis further confirmed that the learned relationships were consistent with known SCR physics, improving confidence in model interpretability and engineering relevance. The developed framework demonstrates that physics-informed feature engineering combined with machine learning can provide a practical and computationally effi cient approach for EATS emission prediction. The results also show clear potential for supporting early-stage virtual evaluation, SCR catalyst sizing, and aftertreat ment system optimisation within industrial engine development.
The Hurdles of Corporate Sustainability Claims A Case Study of Building Automation and Energy Savings
(2026) Gustavsson, Edvin; Zetterling, Knut
As the building sector contributes roughly 40% of global energy consumption, the twin transition of digitalization and green transformation has positioned building automation as a critical tool for decarbonization. However, hardware manufacturers face a significant challenge because current reporting standards, such as the GHG Protocol, fail to capture the potentially positive environmental impacts of "saved emissions" from the use of certain products. This creates a friction between the commercial need for organizational legitimacy in sustainability communication and the complex, non-linear reality of building physics, which can give rise to organized hypocrisy or greenwashing even where no deception is intended. This study employs an abductive research strategy and a case study of Bemsiq Group AB to explore these challenges. We used interviews, focus group discussions and a novel "white paper simulation" to document the specific frictions that arise when translating technical performance data into sustainability claims. The findings reveal that credible, aggregated sustainability disclosure has a hurdle in structural data gaps, such as the absence of standardized baselines and limited access to end-user operational data. Our "Friction Log" identifies conflicts between the frame of engineers prioritizing accuracy, and the business case frame of sales teams and management seeking cognitive clarity for customers. While the frictions are numerous, double-counting and attribution dilemmas are the main hurdles. To navigate these, we propose a shift toward a bottom-up strategy centered on micro-level validation through specific customer cases. This approach allows companies to secure legitimacy with verifiable evidence while advancing more transparent communication standards for the industry.