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Synthesis and Characterization of Barium Titanate and Barium Indate-Zirconate Perovskite Oxyhydrides

(2025) Johansson, Isac

The global imperative to reduce CO2 emissions has driven interest in catalytic conversion technologies, particularly CO2 hydrogenation, which transforms CO2 into valuable chemicals. This reaction often relies on metallic nanoparticles supported on catalyst substrates, commonly metal oxides like Al2O3 or ZrO2. Perovskite oxides have emerged as promising alternatives due to their adjustable surface chemistry, thermal stability, and ability to host redox-active defect sites. Recent attention has turned to anion-adjusted perovskite materials, amongst them oxyhydrides, where oxygen anions are partially replaced by hydride ions. These modifications can enhance catalytic performance and introduce properties such as hydride ion conductivity and interesting electronic and magnetic properties. This project focused on the synthesis and structural analysis of reduced perovskite oxides of synthesised barium titanate (BaTiO3), nano-crystalline barium titanate and barium indate-zirconate (BaZr1−xInxO3− x2 ). For BaTiO3, synthesis routes mainly investigated reduction with CaH2 enclosed in stainless steel capsules, filled with high purity argon. For BaZr1−xInxO3− x2 , reduction by H2 gas annealing was investigated. Characterization heavily relied on powder X-ray diffraction (PXRD) and thermogravimetric analysis (TGA) measurements. Inelastic neutron scatterin (INS) was performed for a reduced 50% indium substituted BaZr0.5In0.5O2.75. The study primarily investigated how synthesis parameters such as molar ratio of CaH2, temperature, and heating time affect reduction extent, anion composition, phase formation, impurity formation and crystallinity. The CaH2 reduction of synthesized tetragonal BaTiO3 at 600◦C for 48 hours yields reduced products with a cubic phase, accompanied by a colour change from white to dark blue or black. An increased molar ratio of CaH2 leads to a greater degree of reduction. Rietveld refinements indicate formation of a single phase in these reduced samples. In contrast, samples of nano-BaTiO3 subjected to the same reduction conditions exhibit a lower degree of reduction and show more pronounced two-phase indications. Higher molar CaH2 ratios result in the formation of Ba2TiO4 impurities. These impurity phases can be reduced by decreasing the CaH2 ratio. For the nano-BaTiO3, a temperature decrease to 580◦ C doesn’t impact Ba2TiO4 amounts. Shortening the heating time to 24 hours leads to decreased amounts, at the expense of a lower reduction extent in the nano-BaTiO3 perovskite phase. Hydrogen annealing of BaZr1−xInxO3− x2 with 50% indium substitution at 800◦ C for 24 hours and 70% substitution at 650◦ C for 20 hours give reduced perovskite oxides of barium indate-zirconate. The extent of reduction is comparable between the two compositions. Inelastic neutron scattering (INS) measurements on the 50% BaZr1−xInxO3−x/2 sample indicate minimal hydride incorporation.

CNG-Electric Hybrid Powertrain for Light Commercial Vehicle. A Technical Evaluation of CNG Internal Combustion Engine, CNG-Electric Hybrid Series and Parallel Powertrains

Chikmagalur Yogeshkumar, Shashank; Jagadish Kamini, Akash

This master’s thesis presents a comprehensive technical evaluation of Compressed Natural Gas (CNG)-electric hybrid powertrains for Light Commercial Vehicles (LCVs), addressing the critical challenge of sustainable urban logistics through advanced simulation and control strategy analysis. Using Siemens Simcenter Amesim, three powertrain architectures conventional CNG Internal Combustion Engine (ICE), series hybrid, and parallel hybrid are rigorously compared across standardized Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and Real Driving Emissions (RDE) cycles, as well as dynamic performance tests. The study integrates sophisticated component models including a Mean Value Engine Model (MVEM) for the CNG engine, Permanent Magnet Synchronous Motors (PMSMs) with Field-Oriented Control, and an Equivalent Consumption Minimization Strategy (ECMS) for real-time energy management. Battery State of Charge (SoC) windows are optimized for each configuration: 55–65% for series hybrids (minimizing degradation) and 20–65% for parallel hybrids (maximizing electric range). Three-Way Catalytic Converters (3WCC) and regenerative braking systems are incorporated to enhance emissions reduction and energy recovery. Results demonstrate that the parallel hybrid configuration achieves superior overall performance, delivering 18–28% fuel savings (4.15–5.89 kg/100 km) and 23–28% CO2 reduction (89.8–126.7 g/km) compared to the conventional ICE baseline, while maintaining robust dynamic performance (0–100 km/h in 12.0 s). The series hybrid excels in acceleration response (7.78 s) and achieves 70% CO reduction under steady-state RDE operation through constant-speed generator operation at peak efficiency (38% brake thermal efficiency). However, elevated CO emissions across all configurations (15–26 g/km, exceeding Euro 6 limits) highlight the need for enhanced aftertreatment strategies, specifically secondary air injection systems. The parallel hybrid emerges as the optimal near-term solution for mixed-duty LCV operations, combining direct mechanical coupling, adaptive ECMS control (average power split ratio 0.67), and effective regenerative braking (approximately 15% energy recovery) to achieve the lowest load sensitivity and most balanced efficiencyperformance trade-off. This work provides actionable insights for fleet operators, policymakers, and automotive engineers pursuing sustainable LCV electrification strategies. With targeted improvements including enhanced aftertreatment, machine learning-augmented ECMS, and optimized battery sizing through probabilistic modeling CNG-electric hybrids demonstrate potential for improved total emission reductions, positioning them as viable bridge technologies supporting EU Green Deal objectives and enabling decarbonized urban logistics by 2030.

Road Friction Aware Adaptive Cruise Control using Robust Nonlinear Model Predictive Control with Uncertainty Quantification

(2025) Crnic, Mubina; Koutsoftas, Sotiris

Road friction is a fundamental factor affecting vehicle safety, especially under adverse weather conditions such as rain, snow or ice. Reduced road grip increases stopping distances and the likelihood of loss of control, contributing to a significant number of traffic accidents each year. While modern Advanced Driver Assistance Systems (ADAS) are designed to enhance driving safety, many systems still lack the ability to adapt their behaviour in real time to varying road friction levels, limiting their effectiveness in low-friction or rapidly changing weather conditions. This thesis addresses this limitation by developing a friction-aware, curvature-adaptive Adaptive Cruise Control (ACC) framework based on a Robust Nonlinear Model Predictive Control (NMPC) formulation. The controller anticipates road curvature and spatially-varying friction by incorporating predicted profiles into the optimization problem. To account for uncertainty, friction is represented through position-dependent bounds in the MPC, while stochastic realizations of friction are generated via Beta sampling and the lead vehicle’s acceleration varies randomly within feasible physical limits. These variations are reflected in dynamic safety constraints that adapt to evolving conditions ahead of the ego vehicle. Soft constraint relaxation is introduced to maintain feasibility under conflicting demands such as sudden friction drops or aggressive lead vehicle deceleration. Despite the nonlinear nature of the problem and the presence of uncertainty, the controller operates using an efficient CasADi-based implementation in MATLAB. Simulation results demonstrate that the proposed framework achieves robust, adaptive cruise control under friction and road curvature changes with stochastic disturbances. The integration of environmental awareness into predictive control enables safer, more responsive vehicle behaviour without sacrificing passenger comfort, and highlights the feasibility of embedding the friction information into future ADAS systems.

Aerodynamisk Optimering för Förbättrad Effektivitet i Airborne Wind Energy-system

(2025) Dubad, Abdirahman; Lakpour, Atrin; Ayman, Mohammad Ali; Amiri, Mohammad; Jaffari, Mustafa; Van Leest, Nils

Detta projekt undersöker olika strategier för att förbättra den aerodynamiska effektiviteten hos linan som är kopplad till draken, med särskilt fokus på att minska luftmotståndskraften. Genom numeriska simuleringar, experiment i vindtunnel och modellering konstaterades en viktig upptäckt: cirka 50 % av det ackumulerade luftmotståndet kommer från de sista 40 meterna av linan, vilket gör denna del mycket lämplig för modulär optimering. I ett annat resultat uppnåddes en uppskattad minskning av det totala luftmotståndet med 51.5 % genom att använda en kombination av en cylindrisk lina täckt med ett aerodynamiskt skal format som en vinge.

Canonicity of the Mahlo Universe

(2025) Kubánek, Ondrej

The thesis presents a proof of canonicity of the external Mahlo universe using the gluing technique. The used metatheory is an extensional type theory extended with indexed induction-recursion to express the reducibility predicate for the external Mahlo universe. This is motivated by the fact that the Mahlo universe can express certain inductive-recursive definitions and so understanding the metatheory for the external Mahlo universe should help with metatheory for induction-recursion.