<p>To fulfill todays requirements on emissions from engines, SCANIA has developed an engine with EGR (Exhaust Gas Recirculation) and VGT (Variable Geometry Turbine). This gives two extra control signals to take into consideration. Open loop optimal control is used to investigate how these two actuators should be controlled to minimize emissions and fuel consumption. A cost function, consisting of the errors between the most important variables and their set points, has been used in the minimization. The variables are the torque, the EGR mass fraction, the oxygen/fuel ratio and the pumping losses.</p><p>From studies of the two control signals in different transients in the engine, information of how to control the VGT and EGR in the optimal way is found. The result from the optimal control has been compared with a PID simulation and has showed a better way to control the signals. The mayor reason why the optimal control is better than a PID controller is the ability to use future values from the transients.</p>

To fulfill todays requirements on emissions from engines, SCANIA has developed an engine with EGR (Exhaust Gas Recirculation) and VGT (Variable Geometry Turbine). This gives two extra control signals to take into consideration. Open loop optimal control is used to investigate how these two actuators should be controlled to minimize emissions and fuel consumption. A cost function, consisting of the errors between the most important variables and their set points, has been used in the minimization. The variables are the torque, the EGR mass fraction, the oxygen/fuel ratio and the pumping losses. From studies of the two control signals in different transients in the engine, information of how to control the VGT and EGR in the optimal way is found. The result from the optimal control has been compared with a PID simulation and has showed a better way to control the signals. The mayor reason why the optimal control is better than a PID controller is the ability to use future values from the transients.

Asteroid mining is rapidly becoming a popular topic amongst space community, primarily due to the potential resources that the asteroids can provide for future spacefaring. One of the interesting resources that can be obtained from asteroids is water, which can also be processed into oxygen and fuel. An intriguing concept would be to process fuel from asteroid, and establish a fuel depot in an Earth-centered orbit. This thesis considers a mission concept consisting of travelling to an Arjuna near-Earth asteroid from a lunar distant retrograde orbit as a depot orbit, processing fuel in-situ from the water on the asteroid, and bringing back 100 tons of fuel to the depot orbit. In order to minimize fuel consumption for such a trip, the thesis develops an optimization method that can obtain the best trajectory for different phases of the round trip, given certain constraints to ensure the spacecraft successfully reaches the asteroid and comes back to the Earth system. The optimization model consists of four steps, i.e., the outbound trip, the first phase of the return trip, the second phase of the return trip, and the optimization for the combined phases of return trip. The outbound trip is the trajectory from the depot orbit to the asteroid. After at least three months of mining, the spacecraft brings back the processed fuel to the vicinity of the Moon. This phase is called the first phase of the return trip. The spacecraft is then captured without an insertion burn to an Earth-centered orbit by a lunar gravity assist maneuver, and travels to the point where the insertion maneuver to the depot orbit begins. This is the second phase of the return trip. The last step of the optimization is the combination of the two phases of return trip, in addition to the final maneuver for entering the lunar distant retrograde orbit. The optimization method uses MATLAB fmincon solver, and it was applied to 29 synthetic asteroids. There were 19 converged solutions, but for 10 asteroids the optimizations was not able to converge. The lowest minimum fuel consumption for a trip is 19965.5 kg, and the highest minimum fuel consumption is 61821.4 kg. For the lowest minimum fuel consumption, the duration of the trip is nearly 7 years, and the duration for the highest minimum fuel consumption is about 2.6 years.

This diploma thesis deals with a design and optimization of mixer truck gearbox bearing arrangements. The gearbox is an important part of mixer truck. Major emphasis is put on design of main bearing which is a key component of the gearbox because of high load applied on it. Two basic criterions were considered in main bearing design – dynamic radial load capacity and contact stress in contact of roller and raceways. Developed two-stage optimizing algorithm is based on standard bearing and allows to design new bearing which is specialized for carrying output shaft of mixer truck gearbox.

Cette thèse est dédiée à la conception optimale de la machine synchrone à rotor bobiné modulaire sans balais pour les systèmes embarqués. Cette machine est basée sur une structure POKIPOKITM développée par Mitsubishi Electric Coopération avec les convertisseurs de puissance intégrée pour augmenter la capacité de tolérance aux défauts. L'analyse électromagnétique est utilisée pour étudier les différentes machines synchrones à rotor bobiné et donc, pour sélectionner la structure qui offre la meilleure tolérance aux défauts et les performances les plus élevées. D’abord, le choix des nombres de phases, d’encoches et de pôles est un point critique. Ensuite, quelques machines sont analysées et comparées selon les critères tels que la densité de couple, le rendement, l'ondulation de couple. La machine avec 7 phases, 7 encoches et 6 pôles est alors choisie. Cette machine est ensuite comparée à la machine synchrone à aimant permanent monté en surface. Le résultat démontre que la machine synchrone à rotor bobiné modulaire sans balais possède le potentiel de remplacer la machine synchrone à aimant permanent dans notre application parce qu’elle présente des performances similaires avec une capacité de tolérance aux défauts élevée. Dans un second temps, une fois la structure 7phases/7encoches/6pôles choisie, cette machine est optimisée en utilisant NOMAD (qui est un logiciel d'optimisation de boîte noire) afin de minimiser le volume externe sous les contraintes électromagnétiques, thermiques et mécaniques. Comme ce problème d'optimisation est extrêmement difficile à résoudre, quelques relaxations ont été effectuées pour tester les différents algorithmes d'optimisation : fmincon (de Matlab) et NOMAD. Nous remarquons que NOMAD est plus efficace que fmincon pour trouver des solutions à ce problème de conception où certaines contraintes sont calculées par des simulations numériques (ANSYS Maxwell ; code éléments finis). En utilisant la méthode NOMAD basée sur l’algorithme Mesh Adaptive Direct Search, nous obtenons des résultats optimaux qui satisfont toutes les contraintes proposées. Il est nécessaire de valider ce design optimisé en vérifiant toutes les contraintes par des simulations électromagnétiques et thermiques en 3D. Les résultats montrent que le couple moyen obtenu par la simulation en 3D est inférieur à la valeur souhaitée. Par conséquent, en augmentant la longueur de la machine, une nouvelle machine corrigée est ainsi obtenue. Nous observons que les pertes de fer obtenues en 3D sont plus élevées qu'en 2D en raison du flux de fuite dans la tête de bobinage. En prenant les valeurs des pertes analysées par la simulation en 3D, la température de surface de la nouvelle machine analysée par la méthode Computational Fluid Dynamics est plus élevée que celle calculée dans l’optimisation. Enfin, un prototype de machine est construit et quelques tests expérimentaux est réalisés. Le résultat montre que la force électromotrice à vide a une forme d'onde similaire par rapport à la prédiction numérique en 3D et la différence de couple statique maximum entre les tests expérimentaux et les simulations par éléments finis en 3D est faible. / This thesis is dedicated to the design and the optimization of modular brushless wound rotor synchronous machine for embedded systems. This machine is constructed based on POKIPOKITM structure with integrated drive electronics. Finite element analysis based optimization becomes more popular in the field of electrical machine design because analytical equations are not easily formalized for the machines which have complicate structures. Using electromagnetic analysis to comparatively study different modular brushless wound rotor synchronous machines and therefore, to select the structure which offers the best fault tolerant capability and the highest output performances. Firstly, the fundamental winding factor calculated by using the method based on voltage phasors is considered as a significant criterion in order to select the numbers of phases, stator slots and poles. After that, 2D finite element numerical simulations are carried out for a set of 15 machines to analyze their performances. The simulation results are then compared to find an appropriate machine according to torque density, torque ripple and machine efficiency. The 7phase/7-slot/6-pole machine is chosen and compared with a reference design surfacemounted permanent magnet synchronous machine in order to evaluate the interesting performance features of the wound rotor synchronous machine. In the second design stage, this machine is optimized by using derivative-free optimization. The objective is to minimize external volume under electromagnetic, thermal and mechanical constraints. Given that an accurate finite element analysis for machine performance takes a long time. Moreover, considering that the average torque can be obtained by simulating the model with only four rotor positions instead of one electric period, optimization strategy is proposed to reduce computational time and therefore, obtain a fast convergence ability by defining relaxed problems which enable minimizing the external volume of the machine under only several constraints such as average torque, torque ripple and copper losses. By testing relaxed problems, two different optimization methods (NOMAD and fmincon) are compared in order to select an appropriate method for our optimization problem. Using NOMAD method based on Mesh Adaptive Direct Search, we achieve optimal results which satisfy all of the constraints proposed. In the third design stage, all constraints are validated by 3D electromagnetic and thermal simulations using finite element and computational fluid dynamics methods. The 3D results show that the average torque obtained is lower than the desired value. By increasing the length of the machine, a new corrected machine is thus obtained. It can be observed that the iron losses obtained in 3D are higher than that in 2D due to the leakage flux in the end-winding. Then, the machine temperature is analyzed by using ANSYS Fluent. Note that the surface temperature is higher than that calculated in the optimization and the coil temperature is 8.48°C higher than the desired value (105°C). However, some dissipation by the shaft and the bearings of the machine are expected to reduce the machine temperature. Finally, a machine prototype is built and some experimental tests are carried out. The results show that the electromotive force has a similar waveform compared to 3D prediction and the difference of the measured and predicted maximum static torques is small.

The increase in electric vehicles and photovoltaic power production may introduce problems to the low-voltage distribution grid. With a higher number of electric vehicles, their accumulated charging power might breach the lowest allowed voltage level of the grid. Photovoltaic-modules can on the other hand exceed the highest allowed voltage level, by producing high accumulated power when the solar irradiance is high. Normally, electric distribution companies in Sweden reinforce the existing grid with more resilient infrastructure, such as stronger and larger cables or transformer stations. This is however a costly and time-consuming solution, which could be solved by using alternative means such as already existing resources. This Master's Thesis investigates how smart charging of batteries can support the low-voltage electrical grid with the increase in electric vehicles and photovoltaic power production. To do this, an optimization tool has been developed in Matlab. An existing model of a low-voltage grid is combined with the developed tool, where controllable batteries and photovoltaic-modules can be placed at specific households in the grid. The controllable batteries belong to either electric vehicles or stationary battery systems, and are intended to support the grid by the means of either reducing peak load powers, voltage variations, or a trade-off between them. Furthermore, this thesis investigates the maximum electric vehicle capability for a specific low-voltage electrical grid in Sweden. From the results, it can be concluded that smart charging of batteries can reduce the peak loads as well as voltage variations. The reduction of voltage variations for the entire low-voltage grid is greatest during the summer, when photovoltaic production generally is at its highest. The results also show that a stationary battery system can reduce the voltage variations to a greater extent, compared to an electric vehicle. Also, the introduction of multiple controllable batteries allows further support of the low-voltage grid. Regarding the maximum electric vehicle capability, the results show that the placement of the vehicles and the charging power strongly affect the maximum number of electric vehicles the low-voltage grid can manage. / Ökningen av elbilar och elproduktion från solceller kan ge problem i lågspänningsnätet. Med ett ökat antal elbilar kan den sammanlagrade effekten vid laddning underskrida den minsta tillåtna spänningsnivån i nätet. Solpaneler kan däremot leda till att den högsta tillåtna spänningsnivån överskrids, genom att producera en hög sammanlagrad effekt när solstrålningen är som högst. Vanligtvis förstärker elnätsbolag i Sverige det befintliga nätet med motståndskraftigare infrastruktur, såsom kraftigare och större kablar eller transformatorstationer. Detta är dock en kostsam och tidskrävande lösning, som skulle kunna lösas med alternativa medel, till exempel redan existerande resurser. Detta examensarbete undersöker hur smart laddning av batterier kan ge stöd till lågspänningsnätet, med en ökning av elbilar samt solcellsproduktion. För att undersöka detta har ett optimeringsverktyg utvecklats i Matlab. En befintlig modell av ett lågspänningsnät har kombinerats med det utvecklade optimeringsverktyget, där styrbara batterier samt solcellsproduktion kan placeras vid specifika hushåll i elnätet. De styrbara batterierna är antingen elbilar eller stationära batterisystem, och är ämnade till att stödja lågspänningsnätet genom att antingen reducera effekttoppar, spänningsvariationer eller en kompromiss av båda. Vidare undersöker detta examensarbete det maximala antalet elbilar som ett specifikt lågspänningsnät i Sverige kan hantera. Resultaten visar att smart laddning av batterier kan reducera effekttoppar samt spänningsvariationer. Reduceringen av spänningsvariationerna för hela lågspänningsnätet visar sig vara högst under sommaren, vilket är då solcellsproduktionen generellt är som högst. Resultaten visar även att stationära batterisystem kan reducera spänningsvariationer ytterligare, jämfört med en elbil. Att introducera flera styrbara batterier tillåter ett ännu större stöd till lågspänningsnätet. Angående det maximala antalet av elbilar som ett lågspänningsnät kan hantera visade resultaten att placeringen av elbilarna samt laddningseffekten har en stor påverkan.