This thesis deals with analysis of electric drive parts from hybrid electric vehicle, namely interior permanent magnet synchronous motor and inverter with control. First part describes a basic theory. Further, motor and inverter computer models are shown, specified simulations are performed on this models. Results are compared with values measured on real machines from accessible literary sources.

Characteristics of synchronous machines with permanent magnets depend among others on geometric layout of the machine section. Unlike EC motors, where rectangular arrangement of quantities is suitable, these machines require sinusoidal behaviour. Specific forming of individual machine parts is partially derived from this requirement. This thesis deals with design of such machines, including various geometrical modifications of their sections and examination of influence of those modifications. The thesis includes also analytical calculation procedure of machine parameters as well as verification of final characteristics using the finite element method. It results in a combination of various design methods. FEMM, Maxwell RMxprt and Maxwell 2D Transient Analysis programs were used for verification. Individual modifications of machine geometries are applied to machines with concentrated windings with different ratio of number of slots and poles, and differences between machines and results of individual methods are compared.

A major portion of this thesis is devoted to the Whisper 200 wind turbine model in Matlab-Simulink environment. The turbine is installed at the Department of Electrical Power Engineering, FEEC BUT. In the model, several types of simulations can be executed. On that basis, the power curve and mathematical relationships between wind speed and other physical quantities, such as RPM, electic current, and voltage, were obtained. Comparisons of the simulations' results with measurement data illustrate adequate agreement, but limitations of the model remain significant, as is exhaustively documented and commented upon in the thesis. As a partial advancement towards elimination of the model's deficiencies, conditions for substantial performance improvements of the dynamic simulation have been elaborately derived.

In this paper, electrification of hydraulic systems is proposed using high-efficiency permanent magnet (PM) motors and wide bandgap power electronic drives. Direct driven hydraulics (DDH) is selected because of its higher efficiency compared to other conventional technologies such as valve-controlled systems. The DDH is directly driven by a servomotor. The ratings and design guidelines for a servomotor used in DDH applications are provided in this paper. Specifically, a surface permanent magnet synchronous machine (SPMSM) is designed. Finally, a state-of-the-art inverter using silicon carbide wide bandgap devices are designed for high performance operation.

In the aviation industry, safety and robustness are the number one priorities, which is why they use well-tested systems such as hydraulic actuators. However, drawbacks such as high weight and maintenance have pushed the industry toward newer, electrical, actuators that are more efficient and lighter. Electrical actuators, on the other hand, have some reliability issues. In particular, short circuits in the stator windings of Permanent-Magnet SynchronousMotors (PMSMs), referred to as Inter-Turn Short Faults (ITSFs), are the dominating faults, and is the focus of this thesis. ITSFs are usually challenging to detect and often do not become noticeable until the fault has propagated, and the motor is on the verge of being destroyed. This thesis investigates the possibility of detecting ITSFs in a PMSM, at an early stage when only one turn is shorted. The method is limited to finding the faults using ML algorithms. Both an experiential PMSM and a simulated model of the experimental PMSM, with the ability to induce an ITSF, were used to collect the data. Several Machine Learning (ML) models were developed, and then trained and tested with the collected data. The results show that four of the tested ML models, being: Random Forest, Gaussian SVM, KNN, and the CNN, all achieve an accuracy exceeding 95%, and that the fault can be found at an early stage in a PMSM with three coils connected in parallel in each phase. The results also show that the ML models are able to identify the ITSF when the simulated data is downsampled to the same frequency as the experimental data. We conclude that the ML models, provided in this study, can be used to detect an ITSF in a simulated PMSM, at an early stage when only one turn is shorted, and that there is great potential for them to detect ITSFs in a physical motor as well.

The importance of accurately controlling the speed and torque of Synchronous Machines (SMs) in industry, transportation, aerospace, to name a few, can not be overstated. The driving unit to control the machines are called Variable Speed Drives (VSDs) and they can be designed in many different ways. In this project, a speed sensorless drive using high-frequency square-wave voltage injection is implemented in a open-source Python software called Motulator, developed by Prof. Marko Hinkkanen at Aalto University. The drive is first tested on an already existing linear model of a Permament Magnet Synchronous Machine (PMSM). An equivalent model is built in Matlab/Simulink to benchmark the performance of the implementation in Python. The results suggest that the performance Motulator implementation is satisfactory when compared to the Simulink implementation. Next, a non-linear Synchronous Reluctance Machine (SynRM) is implemented, using data from Finite Element (FEM) simulations of the non-linear flux-current relation. By using the injection scheme (with some tweaks), the speed of the motor is accurately controlled, but a steady-state position error is observed at all operating points. The error is produced due to the cross-saturation effect and a compensation strategy is implemented in an attempt to remove this error. however without full success. / Det är av avgörande betydelse att kunna kontrollera varvtal och vridmomentet hos synkrona elektriska maskiner (SM) inom transport, flyg och rymd, för att nämna några tillämpningar. Drivsystem för att styra de elektriska maskinerna kallas för varvtalsreglerade drivsystem och kan konstrueras på många olika sätt. I det här projektet implementeras ett varvtalsreglerat drivsystem, utan sensor för mätning av varvtalet. Varvtalsestimeringen bygger på att en fyrkantsvåg med hög frekvens injiceras varur det är möjligt att estimera hastigheten. Implementering görs i Python i en open-source programvara kallad Motulator, utvecklad av professor Marko Hinkkanen från Aaltouniversitetet. Regleringen testas först på en redan existerande linjär modell av en permanentmagnetiserad motor. Som jämförelse utvecklas även en motsvarande implementering av regleralgoritm och motor i Matlab/Simulink. Resultaten visar att Motulatorimplementeringen fungerar väl och att simuleringarna stämmer väl överens med Matlab/Simulink-modellen. I nästa steg implementeras en icke-linjär modell av en synkron reluktansmaskin. Det icke-linjära förhållande mellan flöde och ström modelleras med hjälp av data från finita elementsimuleringar (FEM). Simuleringar i Motulator visar att varvtalet i denna motormodell kan kontrolleras för alla olika laster och varvtal. Däremot noteras ett stationärt rotorpositionsfel vid vissa driftpunkter. Felet beror på mättningen av statorinduktansen och en algoritm implementerats för att kompensera effekten av mättningen och därmed eliminera felet. Det visar sig dock att kompenseringsalgoritmen endast fungerar vid vissa driftpunkter.

Permanent magnet synchronous machines have various advantages and have showed the most superiorperformance for Electric Vehicles. However, modeling them is difficult because of their nonlinearity. In orderto deal with the complexity, the artificial neural network and machine learning models including k-nearest neighbors, decision tree, random forest, and multiple linear regression with a quadratic model are developed to predict electrical parameters and losses as new prediction approaches for the performance of Volvo Cars’ electric vehicles and evaluate their performance. The test operation data of the Volvo Cars Corporation was used to extract and calculate the input and output data for each prediction model. In order to smooth the effects of each input variable, the input data was normalized. In addition, correlation matrices of normalized inputs were produced, which showed a high correlation between rotor temperature and winding resistance in the electrical parameter prediction dataset. They also demonstrated a strong correlation between the winding temperature and the rotor temperature in the loss prediction dataset.Grid search with 5-fold cross validation was implemented to optimize hyperparameters of artificial neuralnetwork and machine learning models. The artificial neural network models performed the best in MSE and R-squared scores for all the electrical parameters and loss prediction. The results indicate that artificial neural networks are more successful at handling complicated nonlinear relationships like those seen in electrical systems compared with other machine learning algorithms. Compared to other machine learning algorithms like decision trees, k-nearest neighbors, and multiple linear regression with a quadratic model, random forest produced superior results. With the exception of q-axis voltage, the decision tree model outperformed the knearestneighbors model in terms of parameter prediction, as measured by MSE and R-squared score. Multiple linear regression with a quadratic model produced the worst results for the electric parameters prediction because the relationship between the input and output was too complex for a multiple quadratic equation to deal with. Random forest models performed better than decision tree models because random forest ensemblehundreds of subset of decision trees and averaging the results. The k-nearest neighbors performed worse for almost all electrical parameters anticipation than the decision tree because it simply chooses the closest points and uses the average as the projected outputs so it was challenging to forecast complex nonlinear relationships. However, it is helpful for handling simple relationships and for understanding relationships in data. In terms of loss prediction, the k-nearest neighbors and decision tree produced similar results in MSE and R-squared score for the electric machine loss and the inverter loss. Their prediction results were worse than the multiple linear regression with a quadratic model, but they performed better than the multiple linear regression with a quadratic model, for forecasting the power difference between electromagnetic power and mechanical power.

Klimatutmaningar, lagändringar och ett ökat miljötänk har tvingat transportsektorn att ställa om till eldrift. Batterier och elmotorer har utvecklats kraftigt och är nu ett alternativ även för tunga fordon. Ett vanligt förekommande problem med elmotorer i fordon är lagerströmmar, här kallat gnista eller blixthändelser. Dessa uppstår på grund av oönskade urladdningar i motorn och förorsakar skador på lagren i motorn. Syftet med motorlager är att avlasta och minska friktionen kring motoraxeln. För att förebygga problemet och se förbättringar eller försämringar av olika åtgärder som görs krävs att lagerströmmar kan identifieras utifrån mätdata. Detta examensarbete analyserar relevant forskning inom området för att sedan introducera en metod och en algoritm för att identifiera lagerströmmar i samarbete med Scania CV. Algoritmen består av tre olika parametrar som påverkar identifieringen av de oönskade strömmarna på olika sätt. Verktyget lyckades identifierade lagerströmmar i olika mätdata, och hittade skillnader i antalet blixthändelser mellan olika körningar av provobjektet. Dock krävs vidare utveckling av verktyget och möjligheten att bearbeta annan typ av data som exempelvis spänningar i motorn för atthitta bättre samband. / A changing climate, changing laws and an increased environmental consciousnesshas forced the transport sector to transition to electric power. Batteries and electric motors have seen a quick and powerful development which means that they are now an alternative even for heavy vehicles. A common problem with electric motors forvehicles is bearing currents. The bearing currents occur as a result of electrical discharges in the motor and can damage the bearings inside the motor. The purpose of motor bearings is to offload and reduce friction for the motor shaft. To prevent the issue and to see improvements or deteriorations from different preventativemeasures it is critical to be able to identify bearing currents from data. This thesis analyzes relevant research in the area before introducing a method and an algorithm for detecting bearing currents in cooperation with Scania CV. The algorithm is composed of three different parameters which affects the identification in different ways. The tool was able to identify bearing currents from various data and found differences between the number of bearing currents between different test runs of the motor. However, more development of the tool and the possibility to process different kinds of data like voltages inside the motor is needed to be able to find better patterns in the data.

Energy efficiency in electric machines is both environmentally and economically important. Water pumps with an integrated Variable Frequency Drive (VFD) and a Permanent Magnet Synchronous Machine (PMSM) have challenged the status quo of the Induction Machine (IM) for water pumps. The Synchronous Reluctance Machine (SynRM) is also a viable alternative that does not use permanent magnets. This degree project focuses on the control of SynRM, with particular attention on sensorless control methods. Simulation models for a SynRM and a Surface Permanent Magnet Synchronous Machine (SPMSM), together with their control systems, were developed in MATLAB Simulink. Two sensorless control methods were implemented and examined: the Luenberger state observer and the Model Reference Adaptive System (MRAS). The control system was adapted to use sensorless control methods. The investigated sensorless control methods allowed the application of the speed-torque test profile for two method and motor type combinations, without causing instability. The Luenberger observer was stable with the SPMSM and the MRAS with the SynRM. Parameter sensitivity with respect to the variation of inductance and stator resistance values used by the sensorless methods was also tested. The Luenberger observer kept the control system stable with up to a ±5 % variation of inductance. The MRAS kept the control system stable down to −12 %, but only up to +2 % for an overestimation of the inductance. The variation of stator resistance had a limited impact on the stability of both sensorless schemes. / Energieffektivisering av elektriska maskiner är betydelsefullt både ur ett miljömässigt och ekonomiskt perspektiv. Vattenpumpar med en synkron permanentmagnetsmaskin och en integrerad enhet för variabel hastighetsreglering (VFD) utmanar den mer konventionella lösningen baserad på en asynkronmaskin (IM). Den synkrona reluktansmaskinen (SynRM) är ett annat lämpligt alternativ som inte använder permanentmagneter. Det här examensarbetet behandlar huvudsakligen sensorlös reglering av SynRM. Simuleringsmodeller för reglering av en SynRM och en permanentmagnetiserad synkronmaskin med ytmonterade magneter (SPMSM) utvecklades i MATLAB Simulink. Två metoder för sensorlös reglering undersöktes, en på Luenbergers tillståndsobservatör och en baserad på adaptiv modellreferensreglering (MRAS). Systemet för reglering anpassades till metoderna för sensorlös reglering. De undersökta sensorlösa metoderna testades vid nominell hastighet med steg i vridmoment och hastighet. Luenberger observatören kunde köras med bibehållen stabilitet med en SPMSM och MRAS kördes med en SynRM, också den med bibehållen stabilitet. Även känsligheten för variationer i induktans och statorresistans för de sensorlösa reglermetoderna testades. Luenberger observatören kunde bibehålla stabiliteten med en variation i induktans på ±5 %. MRAS kunde bibehålla stabiliteten ner till en estimering av induktansen på −12 % av induktansen. På uppsidan nådde dock MRAS stabilitet endast upp till en överestimering av induktansen på 2 %. Felestimering av statorresistansen hade minimal påverkan på stabiliteten för båda sensorlösa reglermetoderna.

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.