Ces dernières années, la fiabilité et la continuité de service des chaînes de traction sont devenus des défis majeurs afin que les véhicules électriques puissent accéder au marché grand public de l’automobile. En effet, la présence de défauts dans les chaînes de traction peut conduire à des dysfonctionnements dans les véhicules et ainsi réduire ses performances par rapport aux véhicules conventionnels. Dans l’hypothèse où des défauts électriques se produisaient, les chaînes de traction des véhicules électriques à pile à combustible devraient inclure des topologies et/ou contrôles tolérants aux défauts pour les différents convertisseurs DC/DC et DC/AC. Dans le cadre de ce travail de recherche, un focus est fait sur le convertisseur DC/DC associé à la pile à combustible de la chaine de traction. Ce dernier doit répondre aux problématiques majeures des applications véhicule électrique à pile à combustible à savoir : faible masse et petit volume, haute efficacité énergétique, réduction de l’ondulation de courant d’entrée et fiabilité. A la base d’une recherche bibliographique poussée sur les structures non-isolées et isolées appropriées pour des applications PàC, une topologie de convertisseur DC/DC entrelacé a été choisie permettant de respecter les contraintes des véhicules électriques à pile à combustible.Ce travail de thèse a ensuite consisté à dimensionner et contrôler la structure de convertisseur DC/DC tolérante aux défauts choisie pour les véhicules à PàC. Des algorithmes de gestion des modes dégradés de ce convertisseur ont été développés et implémentés expérimentalement. A ce titre, l’interaction PàC-convertisseur DC/DC a été étudiée. Une approche théorique, de simulation et expérimentale a été mise en oeuvre pour mener à bien ce travail. / Over the last years, reliability and continuity of service of powertrains have become major challenge so that the fuel cell electric vehicles (CFEV) can access to the mass automotive market. Indeed, the presence of faults in powertrains can lead up to malfunctions in the vehicle and consequently reduce its performances compared with conventional vehicles. In the case of electrical faults, powertrains of FCEV have to include fault tolerant topology and/or control for the different DC/DC and DC/AC converters. Within the framework of this research work, the study is focused on DC/DC converter combined with a Proton Exchange Membrane Fuel Cell (PEMFC). The DC/DC converter must respond to challenging issues in FCEV applications such as: low weight and small volume, high energy efficiency, fuel cell current ripple reduction and reliability. Basing on a thorough bibliographical study on non-isolated and isolated DC/DC converter topologies, an interleaved DC/DC boost converter has been chosen, meeting the FCEV requirements.The purpose of this thesis has then consisted in sizing and controlling the chosen fault-tolerant DC/DC converter topology for FCEVs. Algorithms for degraded mode management of this converter have been developed and implemented experimentally. As such, the interaction between PEMFC and interleaved DC/DC boost converter has been investigated. A theoretical approach, simulation and experimental results have been carried out to complete this work.

This thesis deals with thermal management aspects of electric machinery used in high-performance  applications  with  particular  focus put  on electric machines designed for hybrid electric vehicle applications. In the first part of this thesis,  new thermal models of liquid (water and oil) cooled electric machines are proposed.  The proposed thermal models are based on a combination of lumped parameter (LP)  and numerical methods. As  a first  case study,  a permanent-magnet  assisted  synchronous reluctance machine (PMaSRM) equipped with a housing water jacket is considered.  Particular focus is put on the stator winding and a thermal model is proposed that divides the stator slot into a number of elliptical copper and impregna- tion layers.  Additionally, an analysis, using results from a proposed simplified thermal finite element (FE)  model representing only a single slot of the sta- tor and its corresponding end winding, is presented in which the number of layers and the proper connection between the parts of the LP thermal model representing the end winding and the active part of winding are determined. The approach is attractive due to its simplicity  and the fact  that it closely models the actual temperature distribution for common slot geometries.  An oil-cooled induction machine where the oil is in direct contact with the stator laminations  is also considered.  Here, a multi-segment structure is proposed that  divides  the  stator,  winding and cooling  system  into  a number  of an- gular  segments.   Thereby,  the  circumferential  temperature  variation  due to the  nonuniform distribution  of the  coolant  in the  cooling  channels  can be predicted. In the  second part  of this  thesis,  the  thermal  impact  of using  different winding impregnation  and steel  lamination  materials  is  studied.   Conven- tional varnish, epoxy and a silicone based thermally conductive impregnation material are investigated and the resulting temperature distributions in three small induction machines are compared. The thermal impact of using different steel lamination materials is investigated by simulations using the developed thermal  model  of the water  cooled  PMaSRM. The  differences  in alloy con- tents and steel lamination thickness are studied separately and a comparison between the produced iron losses and the resulting hot-spot temperatures is presented. Finally, FE-based approaches  for  estimating  the  induced  magnet  eddycurrent losses in the rotor of the considered PMaSRM are reviewed and compared in the  form  of a case  study  based on simulations.   A  simplified three-dimensional  FE model  and an analytical  model,  both  combined  with time-domain 2D FE analysis, are shown to predict the induced eddy current losses with a relatively good accuracy compared to a complete 3D FE based model.  Hence, the two simplified approaches are promising which motivates a possible future experimental verification. / <p>QC 20130528</p>

This thesis (1) reports the development of simulation tools and control strategies for optimizing hybrid electric vehicle (HEV) energy management, and (2) reports the design and testing of a hydraulic hybrid school bus (HHB) prototype. A hybrid vehicle is one that combines two or more energy sources for use in vehicle propulsion. Hybrid electric vehicles have become popular in the consumer market due to their greatly improved fuel economy over conventional vehicles. The control strategy of an HEV has a paramount effect on its fuel economy performance. In this thesis, backward-looking and forward-looking simulations of three HEV architectures (parallel, power-split and 2-mode power-split) are developed. The Equivalent Cost Minimization Strategy (ECMS), which weights electrical power as an equivalent fuel usage, is then studied in great detail and improvements are suggested. Specifically, the robustness of an ECMS controller is improved by linking the equivalence factor to dynamic programming and then further tailoring its functional form. High-fidelity vehicle simulations over multiple drive-cycles are performed to measure the improved performance of the new ECMS controller, and to show its potential for online application. While HEVs are prominent in the consumer market and studied extensively in current literature, hydraulic hybrid vehicles (HHVs) only exist as heavy utility vehicle prototypes. The second half of this thesis reports design, construction, and testing of a hydraulic hybrid school bus prototype. Design considerations, simulation results, and preliminary testing results are reported, which indicate the strong potential for hydraulic hybrids to improve fuel economy in the school bus vehicle segment.

In recent decades there has been a growing global concern with regards to vehicle-generated green house gas (GHG) emissions and the resulting air pollution. Currently, gasoline and diesel are the most widely used automotive fuels and are refined from crude oil which is a nonrenewable resource. When they are combusted in an Internal Combustion Engine (ICE) they release significant amounts of air pollutants and Green House Gasses (GHG’s), such as NOx, CO2, SOx, CO, and PM10 into the atmosphere. The results of a feasibility study indicate that intermediary automotive propulsion systems are needed in order to begin a transition from fossil fuels to a clean, renewable transportation system. The Extended Range Electric Vehicle (E-REV) has been identified as an ideal intermediate vehicle technology. In this context, the objective of this thesis is to establish the scientific and engineering fundamentals for the design and development of a Dual-Fuel (hydrogen + Gasoline) Power Generation System for the E-REV sustainable mobility architecture. The devised power generation system is comprised of hydrogen and gasoline storage reservoirs, their respective fuelling systems, a Spark Ignition Internal Combustion Engine (SI ICE), an electric generator, batteries, as well as supplementary electronic systems. The batteries are used to provide power directly to the electric motors and are recharged with both the on-board electric generator and via plug-in capabilities. The developed prototype vehicle, which used a commercial Dune Buggy as a test bed, combined with the on-board rechargeable LiFePO4 battery pack, can provide the users with a daily commute range of ~ 65 [km] relying solely on the battery’s electric power, whereas for longer duration trips the use of the on-board generator would be necessary. The developed Dual-Fuel E-REV power generation system offers the following benefits when compared to the original gasoline ICE architecture: reduced emissions, improved acceleration (47% ↑), improved range (75% ↑), improved fuel economy (22% ↑) and decreased average fuel cost/km (29% ↓).

The world faces the unprecedented challenge of the need change to a new energy era. The introduction of distributed renewable energy and storage together with transportation electrification and deployment of electric and hybrid vehicles, allows traditional consumers to not only consume, but also to produce, or store energy. The active participation of these so called "prosumers", and their interactions may have a significant impact on the operations of the emerging smart grid. However, how these capabilities should be integrated with the overall system operation is unclear. Intelligent energy management systems give users the insight they need to make informed decisions about energy consumption. Properly implemented, intelligent energy management systems can help cut energy use, spending, and emissions. This thesis aims to develop a consumer point of view, user-friendly, intelligent energy management system that enables vehicle drivers to plan their trips, manage their battery pack and under specific circumstances, inject electricity from their plug-in vehicles to power the grid, contributing to frequency regulation.

Transportation is one of the sectors that directly touches the major challenges that energy utilities are faced with, namely, the significant increase in energy demand and environmental issues. In view of these concerns and the problems with the supply of oil, the pursuit of alternative fuels for meeting the future energy demand of the transport sector has gained much attention. The future of transportation is believed to be based on electric drives in fuel cell vehicles (FCVs) or plug-in electric vehicles (PEVs). There are compelling reasons for this to happen: the efficiency of electric drive is at least three times greater than that of combustion processes and these vehicles produce almost zero emissions, which can help relieve many environmental concerns. The future of PEVs is even more promising because of the availability of electricity infrastructure. Furthermore, governments around the world are showing interest in this technology by investing billions of dollars in battery technology and supportive incentive programs for the customers to buy these vehicles. In view of all these considerations, power systems specialists must be prepared for the possible impacts of these new types of loads on the system and plan for the optimal transition to these new types of vehicles by considering the electricity grid constraints. Electricity infrastructure is designed to meet the highest expected demand, which only occurs a few hundred hours per year. For the remaining time, in particular during off-peak hours, the system is underutilized and could generate and deliver a substantial amount of energy to other sectors such as transport by generating hydrogen for FCVs or charging the batteries in PEVs. This thesis investigates the technical and economic feasibility of improving the utilization of electricity system during off-peak hours through alternative-fuel vehicles (AFVs) and develops optimization planning models for the transition to these types of vehicles. These planning models are based on decomposing the region under study into different zones, where the main power generation and electricity load centers are located, and considering the major transmission corridors among them. An emission cost model of generation is first developed to account for the environmental impacts of the extra load on the electricity grid due to the introduction of AFVs. This is followed by developing a hydrogen transportation model and, consequently, a comprehensive optimization model for transition to FCVs in the context of an integrated electricity and hydrogen system. This model can determine the optimal size of the hydrogen production plants to be developed in different zones in each year, optimal hydrogen transportation routes and ultimately bring about hydrogen economy penetration. This model is also extended to account for optimal transition to plug-in hybrid electric vehicles (PHEVs). Different aspects of the proposed transition models are discussed on a developed 3-zone test system. The practical application of the proposed models is demonstrated by applying them to Ontario, Canada, with the purpose of finding the maximum potential penetrations of AFVs into Ontario’s transport sector by 2025, without jeopardizing the reliability of the grid or developing new infrastructure. Applying the models to this real-case problem requires the development of models for Ontario’s transmission network, generation capacity and base-load demand during the planning study. Thus, a zone-based model for Ontario’s transmission network is developed relying on major 500 and 230 kV transmission corridors. Also, based on Ontario’s Integrated Power System Plan (IPSP) and a variety of information provided by the Ontario Power Authority (OPA) and Ontario’s Independent Electricity System Operator (IESO), a zonal pattern of base-load generation capacity is proposed. The optimization models developed in this study involve many parameters that must be estimated; however, estimation errors may substantially influence the optimal solution. In order to resolve this problem, this thesis proposes the application of robust optimization for planning the transition to AFVs. Thus, a comprehensive sensitivity analysis using Monte Carlo simulation is performed to find the impact of estimation errors in the parameters of the planning models; the results of this study reveals the most influential parameters on the optimal solution. Having a knowledge of the most affecting parameters, a new robust optimization approach is applied to develop robust counterpart problems for planning models. These models address the shortcoming of the classical robust optimization approach where robustness is ensured at the cost of significantly losing optimality. The results of the robust models demonstrate that with a reasonable trade-off between optimality and conservatism, at least 170,000 FCVs and 900,000 PHEVs with 30 km all-electric range (AER) can be supported by Ontario’s grid by 2025 without any additional grid investments.

Transportation is one of the sectors that directly touches the major challenges that energy utilities are faced with, namely, the significant increase in energy demand and environmental issues. In view of these concerns and the problems with the supply of oil, the pursuit of alternative fuels for meeting the future energy demand of the transport sector has gained much attention. The future of transportation is believed to be based on electric drives in fuel cell vehicles (FCVs) or plug-in electric vehicles (PEVs). There are compelling reasons for this to happen: the efficiency of electric drive is at least three times greater than that of combustion processes and these vehicles produce almost zero emissions, which can help relieve many environmental concerns. The future of PEVs is even more promising because of the availability of electricity infrastructure. Furthermore, governments around the world are showing interest in this technology by investing billions of dollars in battery technology and supportive incentive programs for the customers to buy these vehicles. In view of all these considerations, power systems specialists must be prepared for the possible impacts of these new types of loads on the system and plan for the optimal transition to these new types of vehicles by considering the electricity grid constraints. Electricity infrastructure is designed to meet the highest expected demand, which only occurs a few hundred hours per year. For the remaining time, in particular during off-peak hours, the system is underutilized and could generate and deliver a substantial amount of energy to other sectors such as transport by generating hydrogen for FCVs or charging the batteries in PEVs. This thesis investigates the technical and economic feasibility of improving the utilization of electricity system during off-peak hours through alternative-fuel vehicles (AFVs) and develops optimization planning models for the transition to these types of vehicles. These planning models are based on decomposing the region under study into different zones, where the main power generation and electricity load centers are located, and considering the major transmission corridors among them. An emission cost model of generation is first developed to account for the environmental impacts of the extra load on the electricity grid due to the introduction of AFVs. This is followed by developing a hydrogen transportation model and, consequently, a comprehensive optimization model for transition to FCVs in the context of an integrated electricity and hydrogen system. This model can determine the optimal size of the hydrogen production plants to be developed in different zones in each year, optimal hydrogen transportation routes and ultimately bring about hydrogen economy penetration. This model is also extended to account for optimal transition to plug-in hybrid electric vehicles (PHEVs). Different aspects of the proposed transition models are discussed on a developed 3-zone test system. The practical application of the proposed models is demonstrated by applying them to Ontario, Canada, with the purpose of finding the maximum potential penetrations of AFVs into Ontario’s transport sector by 2025, without jeopardizing the reliability of the grid or developing new infrastructure. Applying the models to this real-case problem requires the development of models for Ontario’s transmission network, generation capacity and base-load demand during the planning study. Thus, a zone-based model for Ontario’s transmission network is developed relying on major 500 and 230 kV transmission corridors. Also, based on Ontario’s Integrated Power System Plan (IPSP) and a variety of information provided by the Ontario Power Authority (OPA) and Ontario’s Independent Electricity System Operator (IESO), a zonal pattern of base-load generation capacity is proposed. The optimization models developed in this study involve many parameters that must be estimated; however, estimation errors may substantially influence the optimal solution. In order to resolve this problem, this thesis proposes the application of robust optimization for planning the transition to AFVs. Thus, a comprehensive sensitivity analysis using Monte Carlo simulation is performed to find the impact of estimation errors in the parameters of the planning models; the results of this study reveals the most influential parameters on the optimal solution. Having a knowledge of the most affecting parameters, a new robust optimization approach is applied to develop robust counterpart problems for planning models. These models address the shortcoming of the classical robust optimization approach where robustness is ensured at the cost of significantly losing optimality. The results of the robust models demonstrate that with a reasonable trade-off between optimality and conservatism, at least 170,000 FCVs and 900,000 PHEVs with 30 km all-electric range (AER) can be supported by Ontario’s grid by 2025 without any additional grid investments.

<p>The introduction of hybrid powertrains has made it possible to utilise unconventional engines as primary power units in vehicles. The free-piston energy converter (FPEC) is such an engine. It is a combination of a free-piston combustion engine and a linear electrical machine. The main features of this configuration are high efficiency and a rapid transient response.</p><p>In this thesis the free-piston energy converter as part of a hybrid powertrain is studied. One issue of the FPEC is the generation of pulsating power due to the reciprocating motion of the translator. These pulsations affect the components in the powertrain. However, it is shown that these pulsations can be handled by a normal sized DC-link capacitor bank. In addition, two approaches to reduce these pulsations are suggested: the first approach is using generator force control and the second approach is based on phase-shifted operation of two FPEC units. The latter approach results in higher frequency and lower amplitude of the pulsations, which reduce the capacitor losses.</p><p>The FPEC start-up requirements are analysed and by choosing the correct amplitude of the generator force during start-up the energy consumption can be minimised.</p><p>The performance gain of utilising the FPEC in a medium sized series hybrid electric vehicle (SHEV) is also studied. An FPEC model suitable for vehicle simulation is developed and a series hybrid powertrain, with the same performance as the Toyota Prius, is dimensioned and modelled.</p><p>Optimisation is utilised to find a lower limit on the SHEV's fuel consumption for a given drivecycle. In addition, three power management control strategies for the FPEC system are investigated: two load-following strategies using one and two FPEC units respectively and one strategy based on the ideas of an equivalent consumption minimisation (ECM) proposed earlier in the literature.</p><p>The results show a significant decrease in fuel consumption, compared to a diesel-generator powered SHEV, just by replacing the diesel-generator with an FPEC. This result is improved even more by using two FPEC units to generate the propulsion power, as this increases the efficiency at low loads. The ECM control strategy does not reduce the fuel consumption compared to the load-following strategies but gives a better utilisation of the available power sources.</p>

In der vorliegenden Arbeit wird ein Modell zur Beschreibung des Energiebedarfs elektrischer Fahrzeuge des ÖPNV auf Basis von Wahrscheinlichkeitsdichten entwickelt, das insbesondere eine Dimensionierung von fahrzeugseitigem Energiespeichersystem und wegseitiger Energieversorgungsinfrastruktur in einem fahrleitungsfreien Betriebskonzept (DockingPrinzip) erlaubt. Im Gegensatz zur deterministischen Energiebedarfsbestimmung ermöglicht die stochastische Modellierung mit einer Kombination aus Markov-Kette und Semi-Markov-Prozess die Berücksichtigung von Zuverlässigkeitsvorgaben im Sinne einer Missionserfüllung. Schließlich kann so die Größe hybrider Fahrzeugenergiespeichersysteme und die Lage von Nachladestationen entlang der Strecke optimiert werden. Die Wirksamkeit der Modellierung wird anhand einer Fallstudie basierend auf Messdaten für ein Straßenbahnfahrzeug demonstriert. Für die Auslegung der wegseitigen Energieversorgungsinfrastruktur werden die Belastungsgänge des Nachladeprozesses in Form von zeitgewichteten Belastungsdauerkurven für charakteristische Netztopographien hergeleitet. Ein Laden des fahrzeugseitigen Energiespeichers aus einer wegseitigen Energie-Vorsammel-Station (Docking-Station) bringt einerseits eine erhebliche Glättung des Leistungsverlaufs beim Energiebezug. Andererseits ist ein elektrischer Anschluss dieser Station an das Niederspannungsnetz in gewöhnlichen städtischen Siedlungsstrukturen innerhalb weniger hundert Meter möglich.

Battery Management System (BMS) is an essential part in battery powered applications where large battery packs are in use. BMS ensures protection, controlling, supervision and accurate state estimation of battery pack to provide efficient energy management. However the particular application determines the accuracy and requirements of BMS where it has to implement; in electric vehicles (EVs) accuracy cannot be compromised. The software part of BMS estimates the states of the battery pack and takes the best possible decision. In EVs one of the key tasks of BMS’s software part is to provide the actual state of charge (SOC), which represents a crucial parameter to be determined, especially in lithium iron phosphate (LiFePO4) batteries, due to the presence of the high hysteresis behavior in the open circuit voltage than other kind of lithium batteries. This hysteresis phenomena appears with two different voltage curves during the charging and discharging process. The value of the voltage that the battery is going to assume during the off-loading operation depends on several factors, such as temperature, loop direction and ageing. In this research work, hybrid method is implemented in which advantages of several methods are achieved by implementing one technique combined with another. In this work SOC is calculated from coulomb counting method and in order to correct the error of SOC, an hysteresis model is developed and used due to presence of hysteresis effect in LiFePO4 batteries. An hysteresis model of the open circuit voltage (OCV) for a LiFePO4 cell is developed and implemented in MATLAB/Simulink© in order to reproduce the voltage response of the battery when no current from the cell is required (no load condition). Then the difference of estimated voltage and measured voltage is taken in order to correct the error of SOC calculated from coulomb counting or current integration method. To develop the hysteresis model which can reproduce the same voltage behavior, lot of experiments have been carried out practically in order to see the hysteresis voltage response and to see that how voltage curve change with the variation of temperature, ageing and loop direction. At the end model is validated with different driving profiles at different ambient temperatures.