Examensarbeten för masterexamen
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- PostSurface coating on cathode materials for environmental-friendly battery manufacturing(2025) Jayakumar, Karthik; Chalmers tekniska högskola / Institutionen för industri- och materialvetenskap; Chalmers University of Technology / Department of Industrial and Materials Science; Klement, Uta; Sun, JinhuaAn increasing need for sustainable energy has put lithium-ion battery in the forefront of the energy race. Traditional batteries cannot meet the energy demands of the future. Moreover, the use of toxic N-Methyl-2-pyrrolidone (NMP) solvent causes high energy consumption, high cost, and environmental concern of the current battery manufacturing process. In order to eliminate the use of NMP solvent during the electrode processing, here the aim of the project is to modify the surface of cathode material and make it favourable for the water-based electrode processing. Considering the stability issue of cathode in water, graphene, which is impermeable to water, was used as coating materials to protect the surface of cathode material. In addition, introducing graphene in the Li-ion battery improves the performance of the battery as it enhances the conductivity and increases the surface area. The graphene-coated cathode materials were characterized by Scanning Electron Microscopy (SEM) and Thermogravimetric Analysis (TGA). The preliminary results demonstrated that the graphene coating could improve the cycling stability and increase the capacity of the lithium-ion battery.
- PostEnd-of-Life Management for Digital Battery Passports in Electric Vehicles(2025) Cota, Eleonora; Ingman, Molly; Chalmers tekniska högskola / Institutionen för industri- och materialvetenskap; Chalmers University of Technology / Department of Industrial and Materials Science; Despeisse, Mélanie; Persson, HannaThe use of batteries increases every year, and it is more important now than ever to transition into a sustainable way of handling them in the right way. This requires traceability along the whole battery value chain, including information from mining raw materials until the battery is recycled. The EU has implemented the battery regulation, stating that a digital battery passport (DBP) will be required from February 2027. The DBP enables traceability of the battery and facilitates the recycling process. However, the DBP system is not yet finalized, and research shows that there is a gap in guidelines for how it should be managed during its end-of-life (EoL). Therefore, this project aims to determine how to manage DBP for Li-ion electric vehicle batteries (EVB) in the EoL phase to enhance traceability, enable a circular economy and enforce a sustainable transition. This project investigates the complete battery value chain to understand the EoL. Through a literature review and interviews, this thesis dives deeper into the management of batteries, appropriate solutions of ending its corresponding DBP and loopholes that may occur. The results show that the responsible economic operator (REO) creating the DBP is the owner and the responsible actor during its whole existence and is the only one that has the right to end it. This means further that independent operators never will have REO responsibilities, on the other hand, they are obliged to update DBP when needed for the battery. A DBP can only end after its product has been recycled and when this occurs, the recycling station will automatically inform the REO to end the DBP. A two-step verification process controls each individual product and informs its status to the corresponding REO and reduces the risk that information for some products would not be documented. For the recyclers who receive information about all products, this thesis presents a solution for module identification (MID), enabling modules to carry information, since the DBP only follows the complete battery pack. With the two-step verification process, the proposal with MID and keeping the responsibility by the REO, the loopholes regarding lack of information on products within the system can be significantly reduced. In conclusion, this thesis provides guidance on how to manage the DBP in the EoL phase based on currently available information and decisions on the topic.
- PostEvaluating the Strategic Value of Maintenance in Swedish Manufacturing – A Survey Study(2024) Khatri, Sagar; Chalmers tekniska högskola / Institutionen för industri- och materialvetenskap; Chalmers University of Technology / Department of Industrial and Materials Science; Ylipää, Torbjörn; Ylipää, TorbjörnThis Master's thesis examines the strategic importance of maintenance in the manufacturing industry, focusing on how annual investments in maintenance influence operational efficiency and financial performance. The research utilizes a mixed-methods approach, combining literature review and surveys conducted across various Swedish manufacturing firms. It assesses the impact of maintenance strategies on annual investments and the required skills for workers, emphasizing the increasing need for continuous training due to rising labor costs associated with skilled personnel. Key findings indicate that firms adopting advanced maintenance strategies, which integrate predictive and proactive techniques supported by modern technologies, significantly reduce operational costs and minimize downtime. These strategies not only enhance the sustainability of manufacturing processes but also address environmental challenges, making a strong case for their broader adoption. However, the study identifies challenges such as the high initial costs of technology and the continuous need for upskilling workers to keep pace with technological advancements. The thesis supports viewing maintenance as a strategic investment within the manufacturing sector, crucial for improving productivity and achieving a competitive advantage while promoting sustainable practices.
- PostA Study on Commuting Therapy, Designing a Human-Vehicle Interaction System to Enhance the Commuting Experience with Emotion-Based Multisensory Strategies(2024) Lu, Xiaonan; Chalmers tekniska högskola / Institutionen för industri- och materialvetenskap; Chalmers University of Technology / Department of Industrial and Materials Science; Aryana, Bijan; Aryana, BijanCommuting is often perceived as mundane and time-consuming. However, commute also offers an opportune space and time for design interventions that can enhance well-being during the daily commute. This thesis project explores the design opportunities to enhance the leisure experience during commuting, with a focus on Jordan’s Four Product Pleasures. This project reimagines commute as a transition period aimed at improving human well-being, transforming it into a more pleasurable experience. As a result, this project introduces an affective human-vehicle interaction system within the smart cabin, employing multisensory emotion regulation strategies, including vision, aroma, music, and haptics. Specifically, this project details the design of a multisensory experience tailored to driver emotional states during commutes. The proposed design was evaluated through a VR car simulator in a qualitative user study, which revealed user preferences for multisensory experiences.
- PostDeployment of an Unsupervised Anomaly Detection Model Using Anomalib and PyTorch, Is it feasible on a low-powered edge-device?(2025) Kunnathupurakkal Subramanian, Sooraj; Hedin, Ludvig; Chalmers tekniska högskola / Institutionen för industri- och materialvetenskap; Chalmers University of Technology / Department of Industrial and Materials Science; Skoogh, Anders; Chen, Siyuan; Marti, SilvanThe deployment of pre-trained unsupervised anomaly detection models on low-cost and low-powered edge devices, specifically the Raspberry Pi 5, is a promising approach for cost effective and scalable solution for real-time monitoring in production environments. This thesis investigates the plausibility and performance of running such models on the RP5, focusing on their ability to accurately detect anomalies in real-time. This thesis addresses the challenges with hard hardware limitations, software configuration, dataset creation and model performance in an edge environment. To enable the training and validation of the model a custom dataset consisting of mugs stained with food coloring to act as anomalies. While the model successfully ran on the RP5 the inference results demonstrated a lack in accuracy with false positives and negatives as-well as a cycle time of 2000-3000 ms per image, was deemed to slow for real-time applications. Although the findings suggest that with further optimizations, such reducing the resolution of input data and further developing the inference script, the cycle time could be significantly reduced. As well as improving the accuracy by reducing the prevalence of false positives and negatives. Thus the model could be an effective solution for real-time anomaly detection.