This thesis mainly focuses on parallel hybrid electric vehicle energy management problems considering fueling control and air path dynamics of a diesel engine. It aims to explore the concealed fuel-saving potentials in conventional energy management strategies, by employing detailed engine models. The contributions of this study lie on the following aspects: 1) Fueling control consists of fuel injection mass and timing control. By properly selecting combinations of fueling control variables and torque split ratio, engine efficiency is increased and the HEV fuel consumption is further reduced. 2) A transient engine model considering air path dynamics is applied to more accurately predict engine torque. A model predictive control based energy management strategy is developed and solved by dynamic programming. The fuel efficiency is improved, comparing the proposed strategy to those that ignore the engine transients. 3) A novel adaptive control-step learning model predictive control scheme is proposed and implemented in HEV energy management design. It reveals a trade-off between control accuracy and computational efficiency for the MPC based strategies, and demonstrates a good adaptability to the variation of driving cycle while maintaining low computational burden. 4) Two methods are presented to deal with the conjunction between consecutive functions in the piece-wise linearization for the energy management problem. One of them shows a fairly close performance with the original nonlinear method, but much less computing time. / Thesis / Doctor of Philosophy (PhD)

This thesis outlines the development of a control system for a P4-P0 Parallel-Through-The-Road Hybrid Electric Vehicle. This project was part of the EcoCAR Mobility Challenge, an Advanced Vehicle Technology Competition, sponsored by the U.S. Department of Energy, MathWorks and General Motors. The McMaster Engineering EcoCAR team is participating in its second iteration, re-engineering a 2019 Chevrolet Blazer to suit a car-sharing service located within the Greater Toronto Hamilton Area. The proposed architecture uses a 1.5L Engine together with a Belted Alternator Starter motor connected to the traditional low voltage system. The rear axle is electrified containing an Electric Machine, a power oriented Battery Pack and team-designed gear reduction as well as a clutch. The whole rear powertrain is operating at high voltage and has no connection to the traditional low voltage system. Fuel economy improvements up to 12% can be expected while maintaining stock performance targets. A vehicle simulation model was built to accompany the vehicle design process. This includes a mathematical representation of all powertrain components, the development of energy management algorithms, the design of the Hybrid Supervisory Controller structure, and validating and discussing gathered results. Furthermore, all necessary controllers were chosen and communication within them was established by designing the serial data architecture. The developed energy management algorithm is customized to utilize the strengths of all components and this specific architecture. A simple rule-based algorithm is used to operate the engine as close as possible to its most fuel efficient operation point at any time. The P4 and P0 motor are used to apply supportive torque to the engine or load the engine with a negative torque. In that way the energy can be regenerated inside the powertrain and charge sustaining operation v can be achieved. Fuel economy and performance targets are used to discuss the assumed performance of the vehicle once re-engineered. The set targets range from city and highway fuel economy to IVM – 60 mph acceleration time. Overall the developed control system suits a car-sharing service with its ability to adapt to the occurring driving situations ensuring a close to optimal operation for any known or unknown driving situation. It focuses on modularity, simplicity and functionality to allow a working implementation in future years of the EcoCAR Mobility Challenge. / Thesis / Master of Applied Science (MASc) / During the re-engineering of a Hybrid Electric Vehicle different expectations must be considered, for example set government fuel economy regulations, defined performance targets, novelty in innovation, stakeholder expectations as well as the used vehicle platform and the available components. The re-engineering process will be done according to the vehicle development process of the EcoCAR Mobility Challenge. Summarized expectations are the use of this vehicle inside a car-sharing service for the Greater Toronto Hamilton Area targeting “Millennials” while focusing on fuel economy improvements and a low cost of ownership. The research shown in this thesis is set by the requirements derived from the expectations mentioned above. One point of interest is achieving a working control system able to operate close to an optimal state to maximize fuel efficiency and ensuring stock vehicle performance targets. Therefore, the control system has to use the electrification components in an intelligent way. Defining what intelligent control of the engine and the electrification components was one of the main challenges. This thesis outlines how developing a control system for a Hybrid Electric Vehicle can be realized while ensuring that all included interests are met. The object of this research contains choosing the necessary controllers, building a sufficient vehicle simulation model, developing the energy management algorithm, validating the model performance and evaluating the gathered results.

A novel belt alternator starter (BAS) is proposed to replace the starter and alternator in a hybrid electric vehicle. The BAS designed utilizes an 18 rotor, 12 stator pole switched reluctance machine (SRM) configuration, with concentrated bar windings wound in parallel. Through iteration of various machine geometry parameters, the SRM can meet the torque and speeds demands over standardized drive cycles described by the US Environmental Protection Agency. / With the depletion of oil wells and changing global climate, a large emphasis is placed on the research, development and adoption of electric vehicles (EVs) to replace vehicles driven by internal combustion engines (ICEs). However the global supply chain is still not ready for such a large demand in EVs; therefore hybrid electric vehicles (HEVs) aim to ease the transition between ICEs and EVs. The research outlined in this thesis investigates the design of a 18 stator, 12 rotor pole (18/12) configuration switched reluctance machine (SRM) utilizing novel technologies for use as a belt alternator starter (BAS) motor in an HEV. Background research on current trends and technologies for electric motors and vehicles is performed before evaluating initial geometry for the motor core to be designed. Initial geometry is brought into JMAG to develop an electromagnetic model and begin the geometry optimization. The 18/12 design process highlights how changes to motor parameters from a geometry and winding standpoint will affect motor performance. After the motor core geometry yields suitable performance, a mechanical design is proposed encompassing the rotary assembly, cooling as well as solutions for mounting. / Thesis / Master of Applied Science (MASc) / Hybrid electric vehicles are becoming more prevalent as stricter restrictions are placed on fuel economy and emissions targets. Full electric vehicles on the other hand have not yet become the standard form of transportation due to the limits on range and infrastructure. Because of this, automotive manufacturers are researching and developing new methods in which they can meet these restrictions and limitations. Switched reluctance motors aim to be a solution to meet these demands while forging a new path by alleviating the demand on rare earth metals for the motor core. In this thesis, a design is proposed to fill an existing role in vehicle electrification best suited for a belted alternator starter.

An increase emphasis has been placed on the automotive industry to develop advanced technology vehicles which meet increasing strict government regulations and standards for emissions and fuel economy while maintaining the safety, performance, and consumer appeal of the vehicle. In response to these requirements, hybrid and electric vehicle technologies have become more complex as the necessity for vehicles with an overall better environmental impact. Modern engineers must understand the current methods used to analyze and evaluate risk with the new hybrid technologies to ensure the continued customer satisfaction and safety while meeting new government and agency standards. The primary goal of this work is to maintain consistent definitions, standards, and protocols for risk analysis using design failure modes and effects analysis. Throughout the entire automotive sector there exist standards for risk analysis and methods for analysis, however these models can be difficult to relate to the atmosphere under which educational competitions occur. The motor system case study within this work aims to allow the process for DFMEA to be simple and easily implemented and understood when it is appropriate to start. After defining the model, an electric motor system for hybrid vehicle is analyzed for mechanical and inverter system risks. The end result being a 32% reduction in motor system risk due to recommended actions for mitigating top motor systems risks for future motor system design and implementation, all to meet customer requirements. This work aims to provide an additional tool that when implemented will accelerate the next generation of automotive engineers. / Master of Science

With the introduction of electronic control units to automotive vehicles, system complexity has increased. With this change in complexity, new standards have been created to ensure safety at the system level for these vehicles. Furthermore, vehicles have become increasingly complex with the push for electrification of automotive vehicles, which has resulted in the creation of hybrid electric and battery electric vehicles. The goal of this thesis is to provide an example of a hazard and operability analysis as well as a hazard and risk analysis for a hybrid electric vehicle. Additionally, the safety standards developed do not align well with educational prototype vehicles because the standards are designed for corporations. The hybrid vehicle supervisory controller example within this thesis demonstrates how to define a system and then perform system-level analytical techniques to identify potential failures and associated requirements. Ultimately, through this analysis suggestions are made on how best to reduce system complexity and improve system safety of a student built prototype vehicle. / Master of Science / With the introduction of electronic control units to automotive vehicles, system complexity has increased. With this change in complexity, new standards have been created to ensure safety at the system level for these vehicles. Furthermore, vehicles have become increasingly complex with the push for electrification of automotive vehicles, which has resulted in the creation of hybrid electric and battery electric vehicles. There are different ways for corporations to demonstrate adherence to these standards, however it is more difficult for student design projects to follow the same standards. Through the application of hazard and operability analysis and hazard and risk analysis on the hybrid vehicle supervisory controller, an example is provided for future students to follow the guidelines established by the safety standards. The end result is to develop system requirements to improve the safety of the prototype vehicle with the added benefit of making design changes to reduce the complexity of the student project.

During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range. / MS

Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption. Optimal control of multiple energy buffers within the vehicle proves aneffective approach for reducing energy consumption. Energy is temporarilystored in a buffer when its cost is small and released when it is relativelyexpensive. An example of an energy buffer is the vehicle body. Before goingup a hill, the vehicle can accelerate to increase its kinetic energy, which canthen be consumed on the uphill stretch to reduce the engine load. The simplestrategy proves effective for reducing fuel consumption. The thesis generalizes the energy buffer concept to various vehicular componentswith distinct physical disciplines so that they share the same modelstructure reflecting energy flow. The thesis furthermore improves widely appliedcontrol methods and apply them to new applications. The contribution of the thesis can be summarized as follows: • Developing a new function to make the equivalent consumption minimizationstrategy (ECMS) controller (which is one of the well-knownoptimal energy management methods in hybrid electric vehicles (HEVs))more robust. • Developing an integrated controller to optimize torque split and gearnumber simultaneously for both reducing fuel consumption and improvingdrivability of HEVs. • Developing a one-step prediction control method for improving the gearchanging decision. • Studying the potential fuel efficiency improvement of using electromechanicalbrake (EMB) on a hybrid electric city bus. • Evaluating the potential improvement of fuel economy of the electricallyactuated engine cooling system through the off-line global optimizationmethod. • Developing a linear time variant model predictive controller (LTV-MPC)for the real-time control of the electric engine cooling system of heavytrucks and implementing it on a real truck. / <p>QC 20160128</p>

Cette thèse s'inscrit dans le cadre de la gestion d'énergie d'un Véhicule Hybride Électrique. Pour ce type de véhicule, l'optimisation énergétique est un enjeu majeur. Cela consiste à calculer les commandes optimales minimisant la consommation énergétique du véhicule sous un nombre fini de contraintes. Deux types de méthodes peuvent être utilisées pour résoudre ce problème d'optimisation. La première méthode et la plus utilisée, la méthode numérique, utilisant des modèles cartographiques basés sur des données. Elle présente deux inconvénients majeurs: temps de calcul et mémoire importants. La deuxième méthode, appelée analytique, qui permet de remédier à ces deux problèmes, a été utilisée dans cette thèse. Plus l'architecture du véhicule devient complexe (plusieurs machines électriques, moteur thermique, élévateur de tension), plus l'intérêt de cette approche sera important. La méthodologie analytique, proposée dans cette thèse, est composée principalement de trois étapes : la modélisation convexe, le calcul analytique des commandes et la validation des commandes analytiques sur un simulateur de véhicule. Cette méthodologie a été appliquée sur les trois configurations possibles du véhicule étudié : parallèle, bi-parallèle et série. Finalement, l'ajout de l'élévateur de tension dans la gestion d'énergie ainsi que l'étude de son impact sur la consommation énergétique du véhicule sont présentés dans le dernier chapitre. Les résultats obtenus en simulation montrent que la méthode analytique a permis de réduire considérablement le temps de calcul tout en ayant une sous-optimalité très faible. / This thesis focuses on the energy management of Hybrid Electric Vehicle. In this type of vehicle, energy optimization is a major challenge. It consists of calculating optimal commands that minimize the vehicle’s energy consumption under a finite number of constraints. The optimization issue could be solved using a digital method or an analytical method. This choice depends on the nature of energy models that monitor the optimization criteria: analytical or maps of experimental measurements. However, this method presents numerous disadvantages. Its calculation is extremely time-consuming for instance. Therefore, the works presented in this thesis were directed in order to develop an analytical solution where the calculation is lesstime consuming. The architecture of the vehicle is complex. In fact, the vehicle contains two electrical machines, a thermal engine and a step-up. These components have all a straight impact on the vehicle’s energy consumption so several optimization variables were defining. Consequently, working on an analytical solution was a natural choice. The proposed analytical methodology consists of three steps: convex modeling, the command analytical calculation as well as the analytical command validation on a vehicle simulator. This methodology was applied to three possible configurations of the studied vehicle: parallel, biparallel and in serial. Finally, the step-up addition to the energy management as well as the study of itsimpact on the vehicle’s energy consumption are presented in the last chapter. The simulation results show that the analytical method reduces considerably the computing time and has an extremely low suboptimality.

O principal objetivo dos veículos híbridos é diminuir o consumo de combustível em relação a veículos convencionais. Para isso, existe a necessidade de realizar a integração dos diferentes sistemas do trem-de-força e coordenar o seu funcionamento através de estratégias de controle. Tais estratégias são desenvolvidas e simuladas em conjunto com um modelo computacional da planta do veículo antes de serem aplicadas em uma unidade de controle eletrônica. O presente estudo tem como objetivo analisar o gerenciamento de energia em um veículo híbrido elétrico não-plugin do tipo série-paralelo visando à diminuição de consumo de combustível. O método de otimização global é utilizado para encontrar as variáveis de controle que resultam no mínimo consumo de combustível em um determinado ciclo de condução. Na primeira etapa, um modelo computacional da planta do veículo e da estratégia de controle não-ótima são criados. Os resultados obtidos da simulação são então comparados com dados experimentais do veículo operando em dinamômetro de chassis. A seguir, o método de otimização global é aplicado ao modelo computacional utilizando programação dinâmica e tendo como objetivo a minimização do consumo de combustível total ao final do ciclo. Os resultados mostram considerável redução do consumo de combustível utilizando otimização global e tendo como variável de controle não só a razão de distribuição de torque mas também os pontos de operação do motor de combustão. Os modelos computacionais criados nesse trabalho são disponibilizados e podem ser usados para o estudo de diferentes estratégias de controle para veículos híbridos. / The main goal of hybrid electric vehicles is to decrease engine emission and fuel consumption levels. In order to realize this, one must perform the powertrain system integration and coordinate its operation through supervisory control strategies. These control strategies are developed in a simulation environment containing the plant model of the powertrain before they can be implemented in a real-time control unit. The goal of this work is to analyze the energy management strategy which minimizes the fuel consumption in a series-parallel non-plugin hybrid electric vehicle. Global optimization is used for finding the control variables that result in the minimum fuel consumption for a specific driving cycle. In a first stage, a computational model of vehicle plant and non-optimal control strategy are created. The results from the simulation are compared against experimental data from chassis dynamometer tests. Next, a global optimization strategy is applied using dynamic programming in order to minimize total fuel consumption at the end of the driving cycle. The results from the optimization show a considerable fuel consumption reduction having as control variables not only the torque-split strategy but also the engine operating points. As contribution from this work, the computational models are made available and can be used for analyzing different control strategies for hybrid vehicles.

L’objectif principal de ce travail est de développer une stratégie de gestion optimale afin d’améliorer l’efficacité énergétique des véhicules hybrides. Ces travaux comportent une partie analyse expérimentale de la mobilité, une partie modélisation numérique et une partie optimisation de la stratégie de gestion énergétique. L’étude de la mobilité a permis de mettre en avant et de quantifier la prédictibilité des trajets, dus à une forte mobilité contrainte. La modélisation dynamique du véhicule, nécessaire à l’étude de stratégie, a été réalisée par Représentation Energétique Macroscopique (REM) qui est une bonne méthode pour ce type d’étude. La stratégie proposée est basée sur le contrôle prédictif (MPC), résolu par une méthode de Programmation Quadratique, et mis en place en s’appuyant sur la prédiction de cycle issu de l’étude expérimentale. Les perspectives d’améliorations de ces travaux se situent au niveau de la consolidation de la base de données, et du niveau de modélisation de la batterie (impact de la thermique et du vieillissement) et du moteur thermique (prise en compte des polluants). / The main objective of this work is to develop an optimal management strategy to improve energetic efficiency of hybrid electric vehicle. This work is composed by a mobility experimental analysis part, a numerical modelization part and an optimization part of the energy management strategy. The study of mobility allow to highligth and quantify the predictibility of trips, due to a constraint mobility.The dynamic modelling of the vehicle which is necesary to study perfomance of strategies, was realized by Energetic Macroscopic Representation (EMR) which is a good methode in this case. The proposed strategy is based on the predictive control (MPC), solve by a method of Programming Quadratic, and set up resting on the cycle prediction determined from the experimental study. The perspectives of improvements of these work are consolidation of the database, and improvement of the battery modelling (imcluding thermal and ageing effects) and of the thermal engine (taken into account by some pollutants).