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Senast inlagda
Thermal Characterization of Large Lithium-ion cells
(2025) Vishwakarma, Rajesh; Selvakumar Rajeswari, Amithesh
Accurate thermal management is critical for battery management systems (BMS) to prevent thermal runaway and optimize performance. The reliability of thermal models depends on precise characterization of material properties and heat generation, yet many existing models rely on assumed or incomplete parameters, limiting accuracy. This study investigates the thermal properties and behavior of 4695 cylindrical lithium-ion cells using a combined experimental and simulation-based approach. A custom-built isothermal calorimeter measured reversible and irreversible heat generation across various C-rates and temperatures, while entropic coefficients were determined using calorimetric and potentiometric methods to ensure consistency and validate the experimental setup. An Electrical Equivalent Circuit Model (EECM), developed with experimental and manufacturer-provided data, was integrated into
a 3D electrothermal COMSOL model to analyze internal temperature distributions and heat propagation. Results show that heat generation rises significantly with increasing C-rate, with
discharging producing up to 36% higher peak heat than charging, highlighting nonlinear scaling and the importance of tailored cooling strategies. Calorimeter calibration
revealed systematic errors at low power, which were effectively mitigated by offset correction and noise filtering, reducing errors to below 4% at moderate to high power levels. Cooling simulations demonstrated that double-sided liquid cooling at a 60° configuration offers the best balance between thermal performance, uniformity, and practical design, outperforming single-sided and high-angle alternatives. Overall, the integrated experimental and modeling framework provides critical insights for the design of safer, more efficient thermal management systems in large-format lithium-ion batteries for electric vehicles.
Understood and Supported by AI
(2025) Månström, Olivia; Nilsson, Tove
In moments when institutional support is absent or insufficient, this thesis examines whether AI chatbots can offer meaningful emotional support for people affected by gender inequalities. Focusing on Genie AI, developed at Chalmers University of Technology, we explore users’ emotions when collaborating with two chatbot personalities: one action-driven, the other empathetic. While participants initially expressed a need for concrete advice, their real-time communication with the chatbots said the opposite. Most felt more positively toward the empathic chatbot, suggesting that compassionate presence mattered more than practical guidance. This challenges claims that chatbots should prioritize instrumental support, and instead supports prior research on the value of empathy and human-like traits in chatbots. Finally, the study considers the ethical considerations of designing emotionally attuned chatbots, including risks of bias, manipulation, and misplaced trust. AI will not fix institutional failures, but it might offer something that feels human when it is needed most.
Evaluating the Impact of Compression on Inverted Index Search Engine Performance
(2025) Kaulio, Hannes; Blom, Martin
Efficient compression of inverted indexes is vital for scalable search engines, yet the literature lacks a comprehensive comparison of both modern integer-specific and dictionary-based codecs. This thesis bridges that gap by integrating VarByte, Simple8b, FOR, PFOR/NewPFOR/FastPFOR alongside LZ4, Snappy, and Zstandard into Apache Lucene Core and rigorously benchmarking their impact on compression ratio, indexing throughput, and query latency. Our systematic evaluation uncovers the distinct trade-offs — integer codecs tend to enable faster indexing at the expense of larger footprints, while dictionary schemes offer stronger space savings with moderate latency overhead. Finally, we distill these insights into a lightweight decision-support selection tree that guides practitioners to the optimal codec choice based on their specific application priorities. Keywords:
Designing a Standardized Marking System for Public Service Drones
(2025) Hagberg, David; Petterzon, Filippa
The increasing interest in drone innovation for public services, such as emergency medical deliveries and early situational awareness, raises new challenges around public trust and transparency. This thesis, conducted in collaboration with Region Västra Götaland (VGR), investigates how a standardised marking system can help communicate the purpose and origin of public service drones to the general public. By aligning the needs of diverse stakeholders, including VGR, drone companies, and citizens, the study aims to reduce ambiguity and increase acceptance of drones into shared public spaces. Using methods including interviews, questionnaires, and iterative prototyping, the project identifies key concerns around surveillance, recognition, and ethical concerns. The resulting design framework, MarkeD, including visual markings such as colour schemes, patterns, and placements of logos, is intended to signal trustworthiness and to differentiate drones from private or military use, as well as support visibility. The findings offer both a practical framework for drone marking and broader insights into designing for societal acceptance in emerging technologies as well as academic discourse on new and controversial technologies.
RANS-based aerodynamic optimization of a pickup truck: performance assessment and verification of add-on improvements
(2025) Piferi, Alessandro
Pickup trucks represent a significant share of the global automotive market yet
remain characterized by poor aerodynamic efficiency due to their inherent bluffbody geometry. This study investigates the aerodynamics of a generic double-cab
pickup truck, in two configurations: the open and closed bed, using steady state
RANS turbulence model and wind tunnel validation. Three turbulence models,
Realizable k − ε, Lag EB k − ε, and SST k − ω were evaluated against experimental
data to determine the most accurate approach for predicting drag trends across
configurations. The Lag EB k−ε model demonstrated superior fidelity in predicting
∆CD trends and was selected for the baseline flow topology study and optimization
phases. The baseline flow topology analysis identified various critical sources of
drag and the generation of a distinct pair of counter-rotating vortex that negatively
impacts the drag generation. Several aerodynamic add-on devices were designed
and assessed, including a sealed cabin-bed gap, a shortened tailgate, and a rearcab spoiler. Results indicate that sealing the cabin-bed gap yields a drag reduction
of approximately 2.0%. The optimal configuration, combining the gap seal with
the spoiler, achieved a drag reduction of 3.2% for the open bed configuration and
2.4% for the closed bed configuration. These findings demonstrate that targeted
management of the cabin wake and gap flow can significantly mitigate form drag
without altering the primary vehicle architecture.