The purpose of this thesis is to design a powertrain of petrol three-cylinder radial aircraft engine with the main parameters specified (bore, stroke, etc.), balancing inertia forces, stress analysis and determining of fatigue of the selected parts.

The purpose of this thesis is design of a six-cylinder engine arrangement with given main parameters (bore, stroke, etc.), powertrain design, possibilities of firing order, balancing inertia forces and its moments, in case of need, balancing shaft design and calculation of torsion vibrations.

The primary objective of this thesis was to design a crankshaft according to given parameters. In this case the thesis also contains the balancing of inertia forces and its moments, modal analysis and calculation of torsion vibrations. The last chapter includes a calculation of the safety factor by using a Finite Element Method.

The paper describes the design of a prototype vehicle used by Dana Holding Corporation as a mobile laboratory for the development of Spicer® PowerBoost® hydraulic-hybrid powertrain technology. A telehandler vehicle was selected due to its versatility. Starting from the high-level requirements, design choices from the powertrain layout to the control architecture are discussed. The hydraulic-hybrid powertrain system is described, and its performance is analyzed based on representative driving cycles.

Predictive Maintenance (PM) has been increasingly adopted in the Automotive industry, in the recent decades along with conventional approaches such as the Preventive Maintenance and Diagnostic/Corrective Maintenance, since it provides many advantages to estimate the failure before the actual occurrence proactively, and also being adaptive to the present status of the vehicle, in turn allowing flexible maintenance schedules for efficient repair or replacing of faulty components. PM necessitates the storage and analysis of large amounts of sensor data. This requirement can be a challenge in deploying this method on-board the vehicles due to the limited storage and computational power on the hardware of the vehicle. Hence, this thesis seeks to obtain low dimensional descriptive features from high dimensional data using Representation Learning. This low dimensional representation will be used for predicting vehicle faults, specifically Turbocharger related failures. Since the Logged Vehicle Data (LVD) was base on all the data utilized in this thesis, it allowed for the evaluation of large populations of trucks without requiring additional measuring devices and facilities. The gradual degradation methodology is considered for describing vehicle condition, which allows for modeling the malfunction/ failure as a continuous process rather than a discrete flip from healthy to an unhealthy state. This approach eliminates the challenge of data imbalance of healthy and unhealthy samples. Two important hypotheses are presented. Firstly, Parallel StackedClassical Autoencoders would produce better representations com-pared to individual Autoencoders. Secondly, employing Learned Em-beddings on Categorical Variables would improve the performance of the Dimensionality reduction. Based on these hypotheses, a model architecture is proposed and is developed on the LVD. The model is shown to achieve good performance, and in close standards to the previous state-of-the-art research. This thesis, finally, illustrates the potential to apply parallel stacked architectures with Learned Embeddings for the Categorical features, and a combination of feature selection and extraction for numerical features, to predict the Remaining Useful Life (RUL) of a vehicle, in the context of the Turbocharger. A performance improvement of 21.68% with respect to the Mean Absolute Error (MAE) loss with an 80.42% reduction in the size of data was observed.

The new emission regulations for new trucks was made to decrease the CO2 emissions by 30% from 2020 to 2030. One of the solutions is hybridizing the truck powertrain with 48V or 600V that can recover brake energy with electrical machines and batteries. The control of this hybrid powertrain is key to increase fuel efficiency. The idea behind this approach is to combine two different power sources, an internal combustion engine and a battery driven electric machine, and use both to provide tractive forces to the vehicle. This approach requires a HEV controller to operate the power flow within the systems. The HEV controller is the key to maximize fuel savings which contains an energy management strategy. It uses the knowledge of the road profile ahead by GPS and maps, and strongly interacts with the control of the cruise speed, automated gear shifts, powertrain modes and state of charge. In this master thesis, the dynamic programming strategy is used as predictive energy management for hybrid electric truck in forward- facing simulation environment. An analysis of predictive energy management is thus done for receding and full horizon length on flat and hilly drive cycle, where fuel consumption and recuperation energy will be regarded as the primary factor. Another important factor to consider is the powertrain mode of the vehicle with different penalty values. The result from horizon study indicates that the long receding horizon length has a benefit to store more recuperative energy. The fuel consumption is decreased for all drive cycle in the comparison with existing Volvo’s strategy.

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Mississippi State University was selected for participation in the EcoCAR 3 Advance Vehicle Technology Competition. The team designed its architecture around the use of two UQM electric motors, and a Weber MPE 850cc turbocharged engine. To combine the three inputs into a singular output a custom gearbox was designed with seven helical gears. The gears were designed to handle the high torque and speeds the vehicle would experience. The use of this custom gearbox allows for a variety of control strategies. By optimizing the torque supplied by each motor, the overall energy consumption of the vehicle could be reduced. Additionally, studies were completed on the engine to understand the effects of injecting water into the engine’s intake manifold at 25% pedal request from 2000-3500 rpm. Overall, every speed showed an optimum at 20% water to fuel ratio, which obtained reductions in brake specific fuel consumption of up to 9.4%.

Towards a sustainable future, more and more powertrains are being electrified today, thus it is important to prevent unwanted failures and secure a reliable operation. Monitoring the internal temperatures of powertrains and keeping them under their thresholds is an important first step. Traditional modeling methods require expert knowledge and complicated modeling. With all the operating information an electric drive can collect nowadays about the whole powertrain, it becomes possible to apply black boxed machine learning to do the temperature estimating job. In this thesis, multiple machine learning algorithms are tested on their ability to estimate temperatures of the rotor fin, the stator winding, the bearing, and the power module case. The tested algorithms range from an ordinary least square to a deep neuron network. For this purpose, about 150 hours of data are recorded by letting the system run under predefined operating conditions. A hyperparameter search is also conducted for each model to find the best configuration. All the algorithms are evaluated by several metrics. It has been found that neuron networks can perform quite well even under fast transient conditions without any expert knowledge. / Mot en hållbar framtid elektrifieras fler och fler drivlinor idag, därför är det viktigt att förhindra oönskade haverier och säkra en tillförlitlig drift. Att övervaka drivlinornas interna temperaturer och hålla dem under sina trösklar är ett viktigt första steg. Traditionella modelleringsmetoder kräver expertkunskap och komplicerad modellering. Med all driftinformation som en elektrisk drivenhet kan samla in nuförtiden om hela drivlinan, blir det möjligt att tillämpa black boxed machine learning för att utföra temperaturuppskattningsjobbet. I den här avhandlingen testas flera maskininlärningsalgoritmer på deras förmåga att uppskatta temperaturer på rotorfenan, statorlindningen, lagret och kraftmodulhuset. De testade algoritmerna sträcker sig från ett vanligt minsta kvadrat till ett djupt neuronnätverk. För detta ändamål registreras cirka 150 timmars data genom att låta systemet köras under fördefinierade driftsförhållanden. En hyperparametersökning görs också för varje modell för att hitta den bästa konfigurationen. Alla algoritmer utvärderas av flera mätvärden. Det har visat sig att neuronnätverk kan fungera ganska bra även under snabba transienta förhållanden utan någon expertkunskap.