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.

Significant advances have been made in the research and development of electric vehicles (EV’s). Along with the major challenge of energy storage, being also addressed is the efficient design of system energy transfer and consumption. This has had the effect of fundamentally changing perspectives across the mobility and transportation sector. Applied predominantly to road-going vehicles, the industrial context of non-road Electric Vehicles (nrEV’s) and specifically the use of manned electric forklift trucks integrated within the production related materials handling system has, to-date, received far less attention. The overarching aim of this research is to examine the impact and potential for the use of contactless occasional recharging of nrEV’s integrated within a manufacturing line, recognising the need to balance the (sometimes competing) demands of delivering sustainable production while exercising environmental responsibility. Meeting the objectives of this research resulted in the development of a location allocation model for electric charging station determination based on a fundamental understanding of the nature and quality of process inherent key performance indicators (KPI’s) as well as comprehensive process and energy monitoring while considering both Lean and Green Management perspectives. The integration of the generated knowledge and information into a generally valid simulation tool for occasional charging system implementation allows to more thoroughly investigate the impact from occasional charging to overall efficiency and sustainability to be realised. An investigation into relevant literature identified the need for specifically generated energy consumption data and confirmed the need for an energy optimisation model specific to the area of production related materials handling. Empirical data collected from repeated standardised materials handling operations within a selected production related materials handling environment resulted in the development of the Standard Energy Consumption Activity tool (SECA). Further work within this pilot study confirmed the tool as capable of generating reliable and valid data and confirmed the SECA tool as a generally applicable benchmark for energy consumption determination in material handling based on fractional process functions. Integrating this approach into a comprehensive process analysis and charging infrastructure optimisation resulted in the development of an Excel-based simulation model. The (Occasional Charging Station Location Model) OCSLM is based upon Maximal Covering Location Modelling and an endogenous covering distance definition in order to simulate process related potentials and optimal charging system implementation allocations, the target being to increase vehicles usable battery energy. A comprehensive case study based upon six individual and one combined data set confirmed the general and wider applicability of the OCSLM model while the application of the model provides a set of novel results. The application demonstrated a theoretical increase in usable battery energy of between 40% and 60% and within the same case study the impact of technology implementation identified that a reduction in battery and system cost of between 5% and 45% can be realised. However, the use of contactless power transfer resulted in an increase in CO2 emissions of up to 6.89% revealing a negative impact to overall ecology from the use of this energy transfer system. Depending on the availability of fast connecting, contact based energy transmission systems, the approach and results of OCSLM have shown to be directly applicable to contact based systems with resulting CO2 emissions decreasing by 0.94% at an energy transfer efficiency of 96%. Further novelty, of benefit to both academic and industry practice, was realised through the framework and information of the research with the provision of SECA as a process function-based and generally applicable energy consumption standard, OCSLM as a Maximal Covering Location Modell with a focus on occasional charging based on an endogenous covering distance and integrating detailed energy and process monitoring into electric charging station allocation, and the methodology for the application of this approach for fast connecting contactless and contact charging models and cases.

Les politiques de réduction de production de CO2 dans les transports et les progrès techniques, comme l’augmentation des capacités des batteries des voitures électriques, ont ouvert de nouvelles perspectives pour la voiture électrique. De plus, les institutions publiques et groupes privés se sont engagés pour inciter et accompagner du déploiement de la voiture électrique. Malgré ce contexte propice, celui-ci peine à dépasser la barre de 1,07% de part de marché des véhicules légers en France. Cette thèse prend le parti d’étudier le sujet du déploiement de la voiture électrique par les entrées « Usage» et « Innovation ». Autrement dit, comment le processus de la construction de l’usage peut-il dialoguer avec le processus d’innovation ? Après avoir éclairé le contexte de l’usager de la voiture par une représentation de son écosystème, nous proposons une méthodologie (RUI) qui sera en capacité de capter, capitaliser et évaluer les trois connaissances (les Représentations (R) -avant usage-, les Utilisations (U) -pendant usage- et les Instrumentalisations (I) -usage sur le long terme-) constitutives de la construction de l’usage chez un individu. L’analyse de ces connaissances permettra de préciser les freins et leviers à l’émergence de la voiture électrique et les conditions dans lesquelles ils agissent. Elle permettra aussi d’identifier les acteurs concernés par les blocages à l’usage de la voiture électrique. Ainsi, nous proposons les éléments constitutifs à l’élaboration de scénarios du déploiement de la voiture électrique sur le territoire du Sillon Lorrain / Reducing CO2 production policies in transport and technical progress, such as electric-car batteries increase in capacity, have opened up new prospects for electric-car. Furthermore, public institutions and private groups have engaged in encouraging and accompanying electric-car deployment. Despite this favourable context for the electric vehicle development, electric-car does not exceed 1.07% of the market share of vehicles in France. This thesis studies the subject of the electric-car deployment by Usage and Innovation inputs. In other words, how the constructing use process could dialogue with the innovation process? We have formalized car’s user context by an ecosystemic representation. Then, we propose a methodology (RUI) which will be able to capture, capitalize and evaluate the three knowledges Representations (R) -before use-, Uses (U) -during use- and Instrumentalizations (I) - long-term use- constitutive of an indidividual use construction. Knowledge analysis will identify brakes and levers of electric-car emergence and conditions within they operate. It permits also to identify actors concerned by the identified use blockages. Thus, we offer the elements to develop electric-car deployment scenarios in the Sillon Lorrain territory

Climate change is a significant issue in today's society as countries work towards decarbonising the economic sectors that contribute to significant greenhouse gas emissions. The electric vehicle (EV) is proposed as a solution to reduce the level of emissions in the transport sector. However, if an EV is powered by an electrical fossil fuelled source, their penetration into the UK market will have minimal mitigating effects, as emissions will simply shift from the transport sector to the energy production sector. Limited research has evaluated the loss of propulsion energy as a result of operating on-board climate control systems, and has focused more on traction energy. Unlike conventional fossil fuelled vehicles, EVs do not produce waste heat to warm the interior space of the vehicle. The present research found that up to 30% of a vehicle's total energy consumed per trip is allocated to heating requirements, thus the present research developed a temperature predicting numerical algorithm to compute indoor cabin temperatures. The vehicle was exposed to ambient climate conditions with an auxiliary heating or cooling system to evaluate this thermal model. The numerical algorithm could predict the temperature of a cabin space under solar space heating conditions with 62% more accuracy than previously developed models when comparing the Root Mean Square Error performance indicator. The presently developed temperature prediction algorithm may be applied to a route planning application, thus indicating the electrical energy required by the vehicle's battery for users to increase or decrease the desired temperature level. Additionally, this study investigated the ability of a renewable energy resource to decarbonise the vehicle's built-in climate control system. Integrating solar panels on the roof and bonnet of an EV to power an auxiliary climate control system reduced the electrical loading required to reach the occupant's thermal comfort. By installing an auxiliary heating system to increase cabin temperature by 2 or 5°C, the present research found that energy consumption of the built-in climate control system was reduced by 22% or 57%, respectively. This illuminates the potential an auxiliary climate control system has in improving the thermal performance of EVs.

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).

The purpose of the present study is to determine the thermal feasibility of an air-cooled power inverter. The inverter circuitry layout is designed in tandem with the thermal management of the devices. The cylindrical configuration of the air-cooled inverter concept accommodates a collinear axial air blower and a cylindrical capacitor with inverter cards oriented radially between them. Cooling air flows from the axial fan around the inverter cards and through the center hole of the cylindrical capacitor. The present study is a continuation of the thermal feasibility study conducted in fiscal year 2009 for the Oak Ridge National Laboratory to design a power inverter with a radial inflow cylindrical configuration. Results in the present study are obtained by modeling the inverter concept in computer simulations using the finite element method. Air flow rate, ambient air temperature, voltage, and device switching frequency are studied parametrically. Inlet air temperature was 50°C for all the results reported. Transient and steady-state simulations are based on inverter current that represents the US06 supplemental federal test procedure from the US EPA. The source of heat to the system comes from the power dissipated in the form of heat from the switches and diodes and is modeled as a function of the voltage, switching frequency, current, and device temperature. Since the device temperature is a result as well as an input variable, the steady-state and transient solution are iterative on this parameter. The results demonstrate the thermal feasibility of using air to cool an axial-flow power inverter. This axial inflow configuration decreases the pressure drop through the system by 63% over the radial inflow configuration, and the ideal blower power input for an inlet air flow rate of 540 cfm is reduced from 936 W to 312 W for the whole inverter. When the model is subject to one or multiple current cycles, the maximum device temperature does not exceed 164°F (327°F) for an inlet flow rate of 270 cfm, ambient temperature of 120°C, voltage of 650 V, and switching frequency of 20 kHz. Although the maximum temperature in one cycle is most sensitive to ambient temperature, the ambient temperature affect decays after approximately half the duration of one cycle. Of the parametric variables considered in the transient simulations, the system is most sensitive to inlet air flow rate.

With automobile propulsion energy sources turning away from petroleum, the evolution of technology naturally lends itself to electrical hybrid vehicle architectures relying on alternatives as a primary electrical energy source. This thesis presents a design solution of a direct-drive and drive-by-wire prototype of a hybrid extended range electric vehicle (EREV) based on a dune buggy test bed. The developed setup eliminates nearly all mechanical inefficiencies in the rear wheel drive transaxle drivetrain. All controls have been purposely designed as a duplicate set to allow for full independent control of both rear wheels in a truly independent architecture. Along with the controls supporting the design, the motors have been mounted in a near wheel fashion to adequately replace a true hub motor setup. In addition, by-wire throttle and by-wireless brakes in a servomechanical fashion have been developed. The by-wireless braking system is used to control regenerative braking for the rear of the vehicle only allowing for the front brakes to be the primary means of braking as well as a mechanical safety redundancy. This design allows for developments in the areas of truly independent electronic differential systems and studies of the effect of near wheel motor setup. The efficiencies gained by the design solutions implemented in this thesis project have shown their ability to be used in a functioning motor vehicle. Direct gains in mechanical efficiency as well as the removal of a non eco-friendly gasoline powertrain have been attained. In addition, an electric architecture has been developed for further research in future studies such as vehicle stability control, traction control and all-wheel-drive architectures.

Recently, mostly due to global warming concerns and high oil prices, electric vehicles have attracted a great deal of interest as an elegant solution to environmental and energy problems. In addition to the fact that electric vehicles have no tailpipe emissions and are more efficient than internal combustion engine vehicles, they represent more versatile platforms on which to apply advanced motion control techniques, since motor torque and speed can be generated and controlled quickly and precisely. The chassis control systems developed today are distinguished by the way the individual subsystems work in order to provide vehicle stability and control. However, the optimum driving dynamics can only be achieved when the tire forces on all wheels and in all three directions can be influenced and controlled precisely. This level of control requires that the vehicle is equipped with various chassis control systems that are integrated and networked together. Drive-by-wire electric vehicles with in-wheel motors provide the ideal platform for developing the required control system in such a situation. The focus of this thesis is to develop effective control strategies to improve driving dynamics and safety based on the philosophy of individually monitoring and controlling the tire forces on each wheel. A two-passenger electric all-wheel-drive urban vehicle (AUTO21EV) with four direct-drive in-wheel motors and an active steering system is designed and developed in this work. Based on this platform, an advanced fuzzy slip control system, a genetic fuzzy yaw moment controller, an advanced torque vectoring controller, and a genetic fuzzy active steering controller are developed, and the performance and effectiveness of each is evaluated using some standard test maneuvers. Finally, these control systems are integrated with each other by taking advantage of the strengths of each chassis control system and by distributing the required control effort between the in-wheel motors and the active steering system. The performance and effectiveness of the integrated control approach is evaluated and compared to the individual stability control systems, again based on some predefined standard test maneuvers.

Plug-in Hybrid Electric vehicle (PHEV) technology has the potential to reduce operational costs, greenhouse gas (GHG) emissions, and gasoline consumption in the transportation market. However, the net benefits of using a PHEV depend critically on several aspects, such as individual travel patterns, vehicle powertrain design and battery technology. To examine these effects, a multi-objective optimization model was developed integrating vehicle physics simulations through a Matlab/Simulink model, battery durability, and Canadian driving survey data. Moreover, all the drivetrains are controlled implicitly by the ADVISOR powertrain simulation and analysis tool. The simulated model identifies Pareto optimal vehicle powertrain configurations using a multi-objective Pareto front pursuing genetic algorithm by varying combinations of powertrain components and allocation of vehicles to consumers for the least operational cost, and powertrain cost under various driving assumptions. A sensitivity analysis over the foremost cost parameters is included in determining the robustness of the optimized solution of the simulated model in the presence of uncertainty. Here, a comparative study is also established between conventional and hybrid electric vehicles (HEVs) to PHEVs with equivalent optimized solutions, size and performance (similar to Toyota Prius) under both the urban and highway driving environments. In addition, breakeven point analysis is carried out that indicates PHEV lifecycle cost must fall within a few percent of CVs or HEVs to become both the environmentally friendly and cost-effective transportation solutions. Finally, PHEV classes (a platform with multiple powertrain architectures) are optimized taking into account consumer diversity over various classes of light-duty vehicle to investigate consumer-appropriate architectures and manufacturer opportunities for vehicle fleet development utilizing simplified techno-financial analysis. / Graduate / 0540 / 0548 / ehtesham@uvic.ca

Recently, mostly due to global warming concerns and high oil prices, electric vehicles have attracted a great deal of interest as an elegant solution to environmental and energy problems. In addition to the fact that electric vehicles have no tailpipe emissions and are more efficient than internal combustion engine vehicles, they represent more versatile platforms on which to apply advanced motion control techniques, since motor torque and speed can be generated and controlled quickly and precisely. The chassis control systems developed today are distinguished by the way the individual subsystems work in order to provide vehicle stability and control. However, the optimum driving dynamics can only be achieved when the tire forces on all wheels and in all three directions can be influenced and controlled precisely. This level of control requires that the vehicle is equipped with various chassis control systems that are integrated and networked together. Drive-by-wire electric vehicles with in-wheel motors provide the ideal platform for developing the required control system in such a situation. The focus of this thesis is to develop effective control strategies to improve driving dynamics and safety based on the philosophy of individually monitoring and controlling the tire forces on each wheel. A two-passenger electric all-wheel-drive urban vehicle (AUTO21EV) with four direct-drive in-wheel motors and an active steering system is designed and developed in this work. Based on this platform, an advanced fuzzy slip control system, a genetic fuzzy yaw moment controller, an advanced torque vectoring controller, and a genetic fuzzy active steering controller are developed, and the performance and effectiveness of each is evaluated using some standard test maneuvers. Finally, these control systems are integrated with each other by taking advantage of the strengths of each chassis control system and by distributing the required control effort between the in-wheel motors and the active steering system. The performance and effectiveness of the integrated control approach is evaluated and compared to the individual stability control systems, again based on some predefined standard test maneuvers.