Syfte – Syftet för denna studie var att utveckla en sensorlös modell som beräknar en estimeradtemperatur i en BLDC-motors statorlindningar, detta för att undersöka hur väl det går attskydda handhållna produkter mot oaktsam användning och för att kunna motverka körningunder höga temperaturer, vilket skulle kunna förlänga livslängden för handhållna produkter. Metod – Denna studie har använt forskningsmetoden Design Science Research för att utvecklaen artefakt som sensorlöst estimerar temperatur i en BLDC-motors statorlindningar.Artefaktens prestanda för den estimerade temperaturen var noggrannhet, precision ochkonvergeringstid, vilket utvärderats genom analys av kvantitativa data som samlats in underolika experiment. Resultat – Den utvecklade artefakten i studien baserades på en kombination av CurrentInjection och Lumped Parameter Thermal Network samt ett Kalman Filter, artefaktensprestanda uppfyllde inte Husqvarna AB:s krav. Artefaktens precision och noggrannhet för att estimera temperatur i en BLDCmotorsstatorlindningar blev 7,2 °C ± 23,8 °C och dess konvergeringstid blev 7,3 sför dess medelvärde och 18,4 s för dess precision. Implikationer – Denna studie och dess resultat kan användas som en hänvisning på hur välen kombination av Current Injection, Lumped Parameter Thermal Network och Kalman Filterkan estimera temperaturen i statorlindningar för BLDC-motorer med en resistans på 20 mΩ,induktans på 10 uH, nominell hastighet på ~20 000 RPM med terminering av typen Delta ochsom är icke salient. Begränsningar – Studiens begränsningar listas nedan. Utvecklingstiden för artefakten utfördes under begränsad tid. Vilket bidragit till mindre optimering av artefakterna. Vilket bidragit till färre iterationer av artefakter. Artefakten utvärderas endast på VESC hårdvara och dess mjukvara som grund.Artefaktens prestanda påverkas av noggrannheten samt precisionen vidmätning av ström och spänning. Experimenten som utfördes var begränsade. Hade intervallen som experimenten utfördes gällande temperatur, hastighetoch dynamisk last varit större skulle artefaktens prestanda kunna bli undersökti mer verkliga förhållanden. En bättre bromsbänk och en klimatkammareskulle använts. Endast en BLDC motor utvärderades. Utvärdering av fler motorer skulle kunna visa på skillnader i prestanda förartefakten mellan olika mindre motorer, det vill säga visa på dess generellatillämpbarhet. Analys av artefaktens prestanda vid enbart konvergerande tillstånd utfördes inte. Vilket skulle kunna visa på om artefaktens prestanda vid enbart konvergeradetillstånd hade uppfyllt Husqvarna AB:s krav. Nyckelord – Estimera Temperatur, BLDC-motor, Statorlindningar, Current Injection,Lumped Parameter Thermal Network, Kalman Filter, VESC / Purpose – The purpose of this study was to develop a sensorless model that calculates anestimated temperature in a stator winding of a BLDC motor, to investigate how well it is possibleto protect handheld products from inadvertent use and to be able to counteract operation underhigh temperatures, which could prolong service life of the handheld products. Method – This study has used the research method Design Science Research to develop anartifact that sensorlessly estimates temperature in a BLDC motor's stator windings. Theperformance of the artifact for the estimated temperature is accuracy, precision, andconvergence time, which was evaluated by analysing quantitative data collected during differentexperiments. Findings – The artifact developed in the study is based on a combination of Current Injectionand Lumped Parameter Thermal Network and a Kalman Filter, the performance of the artifactdid not meet Husqvarna AB's requirements. The precision and accuracy of the artifact for estimating temperature in a statorwinding of a BLDC motor was 7,2 °C ± 23,8 °C and its convergence time was 7,3 sfor its mean and 18,4 s for its precision. Implications – This study and its results can be used as a reference regarding how well acombination of Current Injection, Lumped Parameter Thermal Network and Kalman Filter canestimate the temperature in stator windings for non-salient BLDC motors with a resistance of20 mΩ, inductance of 10 uH, nominal speed of ~20 000 RPM with termination of the Deltatype. Limitations – The limitations of the study are listed below. The development time for the artifact was performed for a limited time. Which has contributed to less optimization of the artifacts. Which has contributed to fewer iterations of artifacts. The artifact is evaluated only on VESC hardware and its software as a basis.The performance of the artifact is affected by the accuracy and precision inmeasuring current and voltage. The experiments performed were limited. Had the intervals at which the experiments were performed regardingtemperature, speed and dynamic load been greater, the performance of theartifact could have been examined in more real conditions. A better brakebench and a climate chamber would be used. Only one BLDC motor was evaluated. Evaluation of more motors could show differences in the performance of theartifact between different smaller motors, that is, show its general applicability. Analysis of the performance of the artifact in convergent states alone was notperformed. Which could show if the performance of the artifact could fulfill HusqvarnaAB's requirements if the analysis were only performed in convergingconditions. Keywords – Estimate Temperature, BLDC Motor, Stator Windings, Current Injection,Lumped Parameter Thermal Network, Kalman Filter, VESC

The energy crisis together with greater environmental awareness, has increased interest in the construction of low energy buildings. Fabric thermal storage systems provide a promising approach for reducing building energy use and cost, and consequently, the emission of environmental pollutants. Hollow core ventilated slab systems are a form of fabric thermal storage system that, through the coupling of the ventilation air with the mass of the slab, are effective in utilizing the building fabric as a thermal store. However, the benefit of such systems can only be realized through the effective control of the thermal storage. This thesis investigates an optimum control strategy for the hollow core ventilated slab systems, that reduces the energy cost of the system without prejudicing the building occupants thermal comfort. The controller uses the predicted ambient temperature and solar radiation, together with a model of the building, to predict the energy costs of the system and the thermal comfort conditions in the occupied space. The optimum control strategy is identified by exercising the model with a numerical optimization method, such that the energy costs are minimized without violating the building occupant's thermal comfort. The thesis describes the use of an Auto Regressive Moving Average model to predict the ambient conditions for the next 24 hours. A building dynamic lumped parameter thermal network model, is also described, together with its validation. The implementation of a Genetic Algorithm search method for optimizing the control strategy is described, and its performance in finding an optimum solution analysed. The characteristics of the optimum schedule of control setpoints are investigated for each season, from which a simplified time-stage control strategy is derived. The effects of weather prediction errors on the optimum control strategy are investigated and the performance of the optimum controller is analysed and compared to a conventional rule-based control strategy. The on-line implementation of the optimal predictive controller would require the accurate estimation of parameters for modelling the building, which could form part of future work.

Optimizing and controlling the heating systems in buildings is one way to decrease their load on the power grid, as well as introduce load flexibility to be used in Demand Response (DR) applications. A requirement in occupied buildings is that the thermal comfort of the residents is guaranteed, making the optimization of heating systems a constrained problem with respect to indoor temperature. Thermal models capable of predicting indoor temperatures over short (24 hour) horizons are one way to guarantee this comfort. The accuracy and computational complexity of these models have the most significant impact on controller performance. The data requirements and the expert knowledge required for model implementation are also important factors, since they determine the development costs and, finally, whether a model is feasible to implement. First a literature study explores current modeling approaches that depend only on time-series sensor data and that are suited for control applications. A modeling type found to be fit for such purposes are grey-box models, specifically physically inspired inverse models whose parameters are estimated based on data, such as Resistance- Capacitance (RC) models. This modeling of a dynamical system approach uses prior information in the form of the assumed physical equations and has the potential to increase the performance on sparse data problems. The simple form of the model also has a low level of complexity, making it well suited for control applications. However, expert knowledge can be needed for choosing the model equations as well as initializing the parameters. Then the effects of varying RC model complexity, parameter initialization, and training data are investigated in the case study. The chosen models are 1R1C, 2R2C, and 3R2C. They are fitted using the Nelder-Mead algorithm and validated using the data collected from the RISE Research Villa. Parameter initializations are varied by two orders of magnitude and then fitted on different data sequences to avoid relying on expert knowledge in model creation. The initializations that converged with the best R2 training fit on all sequences were deemed reasonable initializations for the problem and used in the training length comparison. The training length of the models varies from 24 to 384 hours. The results showed that increased training data length correlates positively with performance up to 192 hours for all models, but further increasing it gave inconclusive results. The higher order models evaluated struggled to beat the simplest model or even the constant prediction baseline in Mean Absolute Error (MAE) performance at all training lengths, indicating either that the models selected are unsuitable or that the data features chosen are unrepresentative of the indoor temperature dynamics. Regardless, the MAE errors presented here are comparable to the outcomes of related works. This is possibly an artifact of this dataset having a low variance in temperature and thus resulting in lower errors, which underlines the importance of the data used in case-studies. / Att optimera och styra värmesystemen i byggnader är ett sätt att minska belastningen på elnätet och införa flexibilitet i belastningen som kan användas i tillämpningar för efterfrågeflexibilitet (Demand Response, DR). Ett krav i bebodda byggnader är att de boendes termiska komfort garanteras, vilket gör optimeringen av värmesystemen till ett begränsat problem med avseende på inomhustemperaturen. Termiska modeller som kan förutsäga inomhustemperaturer på kort sikt (24 timmar) är ett sätt att garantera denna komfort. Dessa modellers noggrannhet och beräkningskomplexitet har störst inverkan på styrningens prestanda. Datakraven och den expertkunskap som krävs för att genomföra modellen är också viktiga faktorer, eftersom de avgör utvecklingskostnaderna och slutligen om en modell är möjlig att implementera. Först görs en litteraturstudie av nuvarande modelleringsmetoder som endast är beroende av tidsserier av sensordata och som lämpar sig för reglertillämpningar. En modelleringstyp som visat sig vara lämplig för sådana ändamål är grey-box-modeller, särskilt fysikaliskt inspirerade inversa modeller vars parametrar estimeras på basis av data, t.ex. RC-modeller (Resistance-Capacitance). Denna modell av ett dynamiskt system modellering använder förhandsinformation i form av de antagna fysiska ekvationerna och har potential att öka prestandan vid problem med begränsad data. Modellens enkla form har också en låg komplexitetsnivå, vilket gör den väl lämpad för kontrolltillämpningar. Expertkunskap kan dock behövas för att välja modellekvationer och initiera parametrarna. Därefter undersöks effekterna av att variera RC-modellens komplexitet, parameterinitialisering och träningsdata i fallstudien. De valda modellerna är 1R1C, 2R2C och 3R2C. De tränas med hjälp av Nelder-Mead-algoritmen och valideras med hjälp av data som samlats in från RISE Research Villa. Initialiseringarna av parametrarna varieras med två storleksordningar och anpassas sedan på olika dataserier för att undvika att förlita sig på expertkunskap vid skapandet av modellerna. De initialiseringar som konvergerade med den bästa träningsanpassningen R2 på alla sekvenser ansågs vara rimliga initialiseringar för problemet och användes i jämförelsen av träningslängden. Modellernas träningslängd varierar mellan 24 och 384 timmar. Resultaten visade att en ökad längd på träningsdata korrelerar positivt med prestanda upp till 192 timmar för alla modeller, men att ytterligare ökning inte ger några entydiga resultat. De utvärderade modellerna av högre ordning hade svårt att överträffa den enklaste modellen eller till och med referensmodellen med konstant prediktion i fråga om genomsnittligt absolut fel (MAE) vid alla träningslängder, vilket tyder antingen på att de valda modellerna är olämpliga eller att de valda datafunktionerna inte är representativa för inomhustemperaturens dynamik. Oavsett detta är de MAE-fel som presenteras här jämförbara med resultaten från relaterade studier. Detta är möjligen en artefakt av att detta dataset har en låg varians i temperaturen och därmed resulterar i lägre fel, vilket understryker vikten av de data som används i fallstudier.

A significant challenge with permanent magnet synchronous motors (PMSMs) is thermal management. Thermal stress can lead to irreversible damage to components, and to enable efficient cooling, a thermal model is needed. In this thesis paper, methods for estimating the hot spot temperature of the windings in PMSMs used in heavy-duty EVs are investigated. The methods include black-box models and lumped parameter thermal network-based models. The results reveal that the implemented models are not sufficient for achieving the desired accuracy, and indicate that more parts of the windings need to be considered.

Implanter une machine électrique dans un véhicule hybride pose avant tout des problèmes d’encombrement. Sous-dimensionner la machine semble légitime compte tenu de l’usage qui en est fait sur véhicule. Par contre, cela suppose que les aspects thermiques soient pris en compte non seulement lors de l’utilisation, mais aussi lors de la conception de la machine. Le phénomène majeur limitant la densité de puissance massique des machines électriques est l’échauffement interne des bobines et des aimants. La modélisation thermique de la machine est complexe compte tenu de la diversité des sources de chaleur et de la coexistence de différents modes de transferts thermiques : conduction dans la matière, convection avec l’eau de refroidissement, conduction, convection et rayonnement dans l’entrefer. En termes de géométrie, si une première approche peut être réalisée en ne considérant que des flux de chaleur radiaux, la composante axiale doit nécessairement être prise en compte dès lors qu’on veut tenir compte aussi des extrémités de machine, et notamment de la chaleur produite par les roulements et les têtes de bobines. Ainsi pour pouvoir analyser pertinemment les transferts thermiques dans la machine, des méthodes numériques de type CFD ont été utilisées pour caractériser le transfert thermique par convection. La caractérisation des échanges thermiques par convection naturelle et forcée a été réalisée à l’intérieur d’une machine synchrone à aimants permanents internes (MAPI). Des relations empiriques ont été proposées afin de prendre en compte le couplage entre la thermique et l’aérodynamique dans les cavités des machines électriques totalement fermées. Afin de valider la pertinence des modèles numériques dans le cadre de ce travail, des mesures thermiques à l’aide des moyens d’essais de l’UTC ont été réalisées. Les résultats de cette étude sont utilisés pour construire des circuits thermiques équivalents qui prennent en compte les phénomènes thermiques complexes dans les machines électriques fermées utilisées dans les véhicules hybrides et électriques. Ces recommandations de conception permettront l’optimisation de l’effort investi pour le refroidissement de la machine électrique dans ses différentes phases de fonctionnement. / The integration of an electrical machine into modern hybrid vehicles is associated with new technical constraints such as the integrability into small volume without losing certainly in performance. Therefore, the development of compacter electrical machines is a well-founded argument for car manufacturers as well as electrical machine designers. On the other hand, this finding assumes that the thermal aspects are undertaken not only during the operation of the electrical machine, but also during the design process. The internal heat generated in different areas impacts strongly the power density and the magnet health which deeply reduce the electrical machine reliability. Heat transfer modeling inside electrical machines is a tricky task because of the strong coupling between the different physics governing their operations. Indeed, the generated losses spread inside the electrical machine through three heat transfer modes which are: conduction (heat diffusion), convection(heat transport) and radiation (heat scattering). In terms of geometry, if a first approach can be carried out by considering only radial heat fluxes, the axially-transferred heat must be undertaken when it is also necessary to consider end caps effects, and particularly the heat released by the bearings. In order to carry out relevantly the thermal analysis of a permanent magnets synchronous machine, CFD based methods are used to characterize the convective heat transfer inside this machine over a large operating range. Both natural and forced convection are analyzed and the corresponding heat transfer coefficients are numerically-estimated. Empirical equations are proposed in order to take into account the coupling between thermal and fluid dynamics inside the cavities of the studied totally-enclosed machine. These correlations are integrated then into a detailed and reduced thermal network. Experimental tests are carried out using a test bench in order to measure temperature distribution in different areas of the electrical machine. Afterward, a comparison between estimated and measured temperatures shows that the results of the numerically-enhanced thermal network are in a good agreement with measurements. Thus, the proposed recommendations based on CFD modeling allow the convective heat transfer to be characterize quickly and precisely. These correlations are useful for upcoming studies dealing with convection inside automotive electrical machines as well as high speed electrical machines.

Les machines électriques jouent un rôle très important dans la conversion d'énergie dans plusieurs applications et domaines. Les contraintes thermiques jouent ainsi un rôle indispensable dans la conception des machines électriques de plus en plus petites et performantes. En effet, la performance des machines électriques est limitée par les températures maximales admissibles dans certaines zones critiques telles que le bobinage, les aimants permanents et les roulements. Deux approches principales peuvent être utilisées pour étudier le comportement thermique de la machine: la méthode nodale ou le circuit à constantes localisées ou les modèles numériques. Dans notre étude, nous proposons d'appliquer la méthode nodale sur une machine électrique intégrée dans un environnement mécatronique complexe. Le modèle thermique développé de la machine est ainsi présenté avec ses différents éléments. En effet, un modèle précis dépend fortement de plusieurs paramètres thermiques tels que les coefficients d'échange convectif, les conductances de contact, les conductivités équivalentes du bobinage, et autres paramètres. En conséquence, des techniques d'analyse de sensibilité sont ensuite appliquées sur le modèle thermique pour identifier les paramètres d'influence significative sur les températures de la machine ainsi que pour la réduction de ce modèle. Ensuite, nous appliquons deux méthodologies d'identification des paramètres thermiques incertains sont développées et appliquées afin de recaler le modèle thermique de la machine. Cette étape permet la validation de ce modèle par rapport à des mesures thermiques sur une machine synchrone à aimants permanents internes installée sur un banc de caractérisation de machine électriques. Finalement, nous intégrons le modèle recalé dans une approche système mécatronique comportant les lois de commande de la machine ainsi que son convertisseur. Ceci permettra ainsi d'étudier l'influence de la température d'une machine électrique sur le système mécatronique complet. / Electric machines play an important role in power conversion in several applications and fields. With the increasing demand for designing lighter and more efficient machines and optimizing the existing structures, thermal analysis becomes a necessary; in fact, the performance of electric machines islimited by the allowable temperatures in many critical components like windings, permanent magnetsand bearings. Two main approaches can be employed in order to study the machine thermal behavior : the lumped parameter thermal network (LPTN) or numerical models. Considering low-computationtime-consuming and the possibility to be integrated in a mechatronics system design, the LPTN method is considered in our study. The latter is mainly applied on electric machine integrated in a complex mechatronics environment. The thermal network is presented along with the definition of the principal elements constituting this network. In fact, an accurate and reliable network strongly depends on many critical parameters like heat transfer coefficients, interface gaps, impregnation goodness, among others. For this reason, different sensitivity analysis techniques are carried out in order to, first, identify the significance of uncertainties in the evaluation of these parameters on machine temperatures and second, to reduce the thermal network. Next, we propose two optimization algorithm-based identification methodologies in order to calibrate results of the thermal network with measured temperatures obtained from a test-bench of a permanent magnet based integrated starter-generator machine. The calibrated model is then integrated in a mechatronics system consisting of an electric model of the electric machine, along with its control strategy and the power converter. This final study allows us to evaluate the impact of the machine temperature rise on the mechatronic system.