<p dir="ltr">The industry's interest in electrified powertrain-equipped vehicles has increased due to environmental and economic reasons. Electrified powertrains, in general, produce lower sound and vibration level than those equipped with internal combustion engines, making noise and vibration (N&V) from other non-engine powertrain components more perceptible. One such N&V type that arouses concern to both vehicle manufacturers and passengers is gear growl, but the signal characteristics of gear growl noise and vibration and the threshold of those characteristics that can be used to determine whether a gear growl requires attention are not yet well understood. This study focuses on developing a method to detect gear-growl based on the N\&V measurements and determining thresholds on various severities of gear-growl using supervised machine learning classification. In general, a machine learning classifier requires sufficient high-quality training data with strong information independence to ensure accurate classification performance. In industrial practices, acquiring high-quality vehicle NVH data is expensive in terms of finance, time, and effort. A physically informed data augmentation method is, thus, proposed to generate realistic powertrain NVH signals based on high-quality measurements which not only provides a larger training data set but also enriches the signal feature variations included in the data set. More specifically, this method extracts physical information such as angular speed, tonal amplitudes distribution, and broadband spectrum shape from the measurement data. Then, it recreates a synthetic signal that mimics the measurement data. The measured and simulated (via data augmentation) are transformed into feature matrix representation so that the N\&V signals can be used in the classification model training process. Features describing signal characteristics are studied, extracted, and selected. While the root-mean-square (RMS) of the vibration signal and spectral entropy were sufficient for detecting gear-growl with a test accuracy of 0.9828, the acoustic signal required more features due to background noise, making data linearly inseparable. The minimum Redundancy Maximum Relevance (mRMR) feature scoring method was used to assess the importance of acoustic signal features in classification. The five most important features based on the importance score were the angular acceleration of the driveshaft, the time derivative of RMS, the tone-to-noise ratio (TNR), the time derivative of the spectral spread of the tonal component of the acoustic signal, and the time derivative of the spectral spread of the original acoustic signal (before tonal and broadband separation). A supervised classification model is developed using a support vector machine from the extracted acoustic signal features. Data used in training and testing consists of steady-state vehicle operations of 25, 35, 45, and 55 mph, with two vehicles with two different powertrain specs: axles with 4.56 and 6.14 gear ratios. The dataset includes powertrains with swapped axles (four different configurations). Techniques such as cost weighting, median filter, and hyperparameter tuning are implemented to improve the classification performance where the model classifies if a segment in the signal represents a gear-growl event or no gear-growl event. The average accuracy of test data was 0.918. A multi-class classification model is further implemented to classify different severities based on preliminary subjective listening studies. Data augmentation using signal simulation showed improvement in binary classification applications. In this study, only gear-growl was used as a fault type. Still, data augmentation, feature extraction and selection, and classification methods can be generalized for NVH signal-based fault diagnosis applications. Further listening studies are suggested for improved classification of multi-class classification applications.</p>

Energy management systems (EMSs), implemented in the electronic control unit (ECU) of an actual vehicle with electri ed powertrain, is a much simpler version of the theoretically developed EMS. Such simpli cation is done to accommodate the EMS within the given memory constraint and computational capacity of the ECU. The simpli cation should ensure reasonable performance compared to theoretical EMS under real-life driving scenarios. The process of simpli cation must be effective to create a versatile and utilitarian EMS. The reinforcement learning-based controllers feature pro table characteristics in optimizing the performance of controllable physical systems as they do not mandatorily require a mathematical model of system dynamics (i.e. they are model-free). Quite naturally, it can aspired to testify such prowess of reinforcement learning-based controllers in achieving near-global optimal performance for energy management system (supervisory) of electri ed powertrains. Before deployment of any supervisory controller as a mainstream controller, they should be essentially scrutinized through various levels of virtual simulation platforms with an ascending order of physical system emulating-capability. The controller evolves from a mathematical concept to an utilitarian embedded system through a series of these levels where it undergoes gradual transformation to finally become apposite for a real physical system. Implementation of the control strategy in a Simulink-based forward simulation model could be the first stage of the aforementioned evolution process. This brief will delineate all the steps required for implementing an reinforcement learning-based supervisory controller in a forward simulation model of a hybrid electric vehicle. A novel framework of loss-minimization based instantaneous optimal strategy is introduced for the energy management system of a multi-mode hybrid electric powertrain in this brief. The loss-minimization strategy is flexible enough to be implemented in any architecture of electrified powertrains. It is mathematically proven that the overall system loss minimization is equivalent to the minimization of fuel consumption. An online simulation framework is developed in this article to evaluate the performance of a multi-mode electrified powertrain equipped with more than one power source. An electrically variable transmission with two planetary gear-set has been chosen as the centerpiece of the powertrain considering the versatility and future prospects of such transmissions. It is noteworthy to mention that a novel architecture topology selected for this dissertation is engendered through a series of rigorous screening process whose workflow is presented here with brevity. One of the legitimate concern of multi-mode transmission is it's proclivity to contribute discontinuity of power-flow in the downstream of the powertrain. Mode-shift events can be predominantly held responsible for engendering such discontinuity. Advent of dynamic coordinated control as a technique for ameliorating such discontinuity has been substantiated by many scholars in literature. Hence, a system-level coordinated control is employed within the energy management system which governs the mode schedule of the multi-mode powertrain in real-time simulation. / Thesis / Doctor of Philosophy (PhD)