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.

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>

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.

This thesis deals withmodeling and control of an electric drive equipped with a permanentmagnet assisted synchronous reluctance (PMSynRel) machine for a plug-in hybrid electric vehicle application. In the first part of the thesis, a special use of the PMSynRel machine in consideration, known as an integrated charger concept, is investigated. The integrated charger feature allows using the PMSynRel machine as a part of the vehicle’s on-board charging system when charging the battery from the grid. A finite-element based analysis is performed providing important insights into the machine operation during the charging process. Dynamic models are developed that facilitate the controller development and the estimation of the efficiency during charging. In the second part of the thesis, position sensorless control of the PMSynRel drive when applied in an automotive application is considered and analyzed thoroughly. First, a fundamental-excitation based rotor-position estimation technique is investigated. The study shows that the impact of current dynamics on the resulting torque dynamics has to be considered in some very demanding applications. Second, focus is put on signalinjection based sensorless control methods. Impacts of nonlinearities, such as magnetic saturation, cross-saturation and inductance spatial harmonics, on sensorless control performance are investigated and methods to improve the sensorless control quality are summarized and presented. An approach to determine the feasible region for operating sensorless at low-speeds without directly measuring the differential inductances is proposed. For the PMSynRel drive in consideration, the achievable maximum torque is limited when operating sensorless following the maximum-torque-per-ampere (MTPA) current reference trajectory at low-speeds. An optimization approach is therefore proposed which extends the output torque when operating sensorless while still maintaining a relatively high efficiency. To initialize the sensorless control correctly from standstill, the impact of the saturated magnetic bridges in the rotor is also investigated. Finally, torsional drive-train oscillations and active damping schemes are considered. An off-vehicle setup for implementing and evaluating different active damping schemes is proposed. Of particular interest for sensorless operation in automotive applications, the impact of slow speed estimation on the possibility to achieve good active damping control is investigated and a design approach that allows the implementation of an active damping scheme using estimated speed is suggested. / <p>QC 20140114</p>

Electric and hybrid vehicles are particularly suited for use in urban areas since city transportation is mainly characterized by relatively short driving distances, low continuous power requirements, long idling times and high availability of regenerative braking energy. These characteristics, when carefully incorporated into the design process, create valuable opportunities for developing clean, efficient and cost effective urban vehicle propulsion systems. In the first part of the thesis, we present data collected in the city of Bangalore, India from a very commonly seen mode of transportation for hire in India and other emerging economies, namely a three-wheeled vehicle known as the “auto-rickshaw”. From a statistical analysis, it is shown that the typical range is 72.5 km with a mean speed of 12.5 km/h. More than 60% of the time the auto-rickshaw is stationary or has a speed of less than 5 km/h. From a model of the auto-rickshaw, it is shown from simulations that 4 kW DC motor and about 10 kWh of electrical energy is enough to meet 80% of typical requirement. Based on this finding, in this thesis, a novel parallel hybrid architecture is proposed where two 2 kW DC hub motors are directly mounted on the wheels and an internal combustion (IC) engine output is connected to the stator of the DC hub motors to provide additional power when required. To match load and speed, a continuously variable transmission (CVT) is placed in-between the IC engine and the DC hub motor. The proposed hybrid configuration adds speed to the wheel output unlike the normal power split configuration which adds torque. One of the main objective of this work is to study and compare the performance of the above novel speed-addition and compare with the typical torque-addition configuration. A MATLAB/Simulink model for both the configurations, with DC hub motor and a small IC engine, has been created and the fuel consumption has been calculated. It is shown that the proposed speed-addition concept gives better fuel efficiency for the standard modified Indian Driving Cycle. The models have also been compared for actual driving data and an optimal control strategy has been developed using dynamic programming. It is again shown that the proposed speed-addition concept results in better fuel economy. In the last part of the thesis, a low cost experimental test-bed consisting of an auto-rickshaw IC engine, a CVT and a 2 kW DC hub motor has been developed to validate the speed-addition concept and compare with the torque-addition configuration. The torque-speed curves of the IC engine, the DC motor and both of them together, in the speed and torque-addition configuration, have been obtained. It is shown that the speed-addition concept does indeed work and the obtained results are significantly different from the torque-addition configuration.

Les principaux objectifs de ce travail est de développer une méthode de modélisation efficace pour un déploiement facile et rapide d'une stratégie de contrôle, d'examiner et d'étudier cette stratégie pour une application spécifique, et d'analyser l'amélioration qui peut être apporté à un moteur pour une meilleure efficacité dans les systèmes électrique et hybride. Ce travail comprend une partie simulation du système étudié et sa validation avec les résultats expérimentaux. Les études de cas sont utilisées pour analyser l'optimisation qui peut être effectuée en comparaison au système d'origine (le véhicule étudié est la NOAO).Un outil d'optimisation est choisie pour optimiser la stratégie de contrôle actuellement déployée sur le véhicule. Cette outil nous a permis de développer une nouvelle stratégie de commande optimisée prêt à être déployé dans le véhicule. Un procédé de prédiction pour connaître la consommation d'énergie du système est mis au point en vue d'obtenir un contrôle optimal adapté à la demande du véhicule et à une utilisation spécifique.Comme perspectives, les principaux composants du système peuvent être étudiés et modélisé afin d'améliorer l’efficacité énergétique du véhicule. La Représentation Energétique Macroscopique (REM) est une bonne méthode pour représenter le modèle dynamique et peut être utilisé pour modéliser des machines électromécaniques. Cette méthode est également envisagé pour modéliser d’autre système que le système véhicule tel que les systèmes énergies renouvelables, les systèmes électromécanique ou robotique. / The main objectives of this work is to develop an effective modeling method for an easydeployment of a control strategy, to review and study an optimal control strategy for a specific application, and to analyze improvement that can be effected to engine for better efficiency in hybrid vehicle architecture. The scopes of this work include the simulation part of the studied system and its validation with experimental results. Study cases are used to analyze optimization that can be effected to the original system. A well established optimization tool is chosen to optimize the actual control strategy and becomes a benchmark of a new optimal control strategy to be deployed in the system. A predictive method to know energy consumption of the system is developed in order to obtain an optimal control suitable with the vehicle application. Using the developed model, analysis is conducted to identify an optimal control strategy for a specific utilization. As perspectives, the main components of the system can be studied for improvements of its energy efficiency. The Energetic Macroscopic Representation (EMR) is a good method to represent dynamic model and it can be used to model any electromechanical machines and can be envisaged to model other system than a vehicle system, like a renewable energy system, a new electro-mechanical system or a robotic system.

Philosophiae Doctor - PhD / Aqueous ion-intercalation batteries (AIB’s) have the potential to provide both high power for hybrid-electric transport, and low cost bulk energy storage for electric grid supply. However, a major setback to AIB development is the instability of suitable ionintercalation anode material in aqueous electrolyte. To counter this problem, the use of activated carbon (AC) (a supercapacitor anode) paired against the low cost ionintercalation cathode spinel LiMn2O4 (LMO) provides a stable alternative. This thesis comprises two novel areas of investigation concerning: (1) the development of the AC/LMO cell for high power applications, and (2) the introduction of PbSO4 as a high capacity alternative anode material paired against LMO for low cost bulk energy storage. The study on AC/LMO explores the electrode combination’s practical specific energy and power capability at high P/E (power to energy ratio) of 50:1 suitable for hybrid electric vehicle batteries. To study the relationship between electrode material loading density, active material performance, and current collector mass contribution, a specially designed cell was constructed for galvanic cycling of different thicknesses of electrode. Between a loading density range of 25 – 100 mg, ~50 mg of total active material between two 1 cm2 current collectors produced the highest 50:1 P/E ratio values of 4 Wh/kg and 200 W/kg, constituting a 4-fold reduction of the active material values of thin films at 50:1 P/E. The cycling potentials of the individual electrodes revealed that doublings of electrode film loading density increased the LMO electrode’s polarization and voltage drop to similar levels as doublings in applied current density. However, by increasing the charging voltage from 1.8 V to 2.2 V, 6 Wh/kg and 300 W/kg was obtainable with minimal loss of energy efficiency. Finally a large-format cell of a calculated 3 Ah capacity at 50:1 P/E was constructed and tested. The cell produced ~60% of the anticipated capacity due to a suspected high level of resistance in the electrode contact points. The overall conclusion to the study was that AC/LMO holds promise for high power applications, and that future use of higher rate capability forms of LMO offers a promising avenue for further research. v The second part of this thesis presents the development of a novel cell chemistry, PbSO4/LMO, that has yet to be reported elsewhere in existing literature. The cell uses aqueous pH 7, 1 M, Li2SO4 electrolyte, and forms an electrode coupling where the PbSO4 anode charge/discharge is analogous to that in Pb-acid batteries. The average discharge voltage of the cell was 1.4 V and formed a flat charge/discharge plateau. The use of a low cost carbon coating method to encapsulate PbSO4 microparticles had a marked improvement on cell performance, and compared to uncoated PbSO4 improved both rate capability and specific capacity of the material. The active materials of the carbon-coated PbSO4/LMO cell produced a specific energy 51.1 Wh/kg, which, if a 65% yield is possible for a practical cell format, equals 38.4 Wh/kg, which is 15 Wh/kg higher than AC/LMO bulk storage cells at 23 Wh/kg, but lower than Pb-acid batteries at ~25-50 Wh/kg. Interestingly, the specific capacity of PbSO4 was 76 mAh/g compared to 100 mAh/g in Pb-acid cells. The predicted cost of the cell, providing a 65% value of the active material specific energy for a practical cell can be realized, is on par with Pb-acid battery technology and, importantly, uses 2.3 × less Pb/kWh. The cycling stability achieved thus far is promising, but will require testing over comparable cycle life periods to commercial batteries, which could be anywhere between 5 – 15 years.

La quasi-totalité des études de machines synchrones à aimants permanents (MSAP) pour les applications aux véhicules hybrides concernent les performances uniquement sur quelques points particuliers d’un cycle de fonctionnement du véhicule (le point de base, le point à grande vitesse ou le point le plus sollicité). Cependant, ces machines électriques fonctionnent souvent à différents couples et à différentes vitesses. Cette thèse s’intéresse donc à l’étude des performances de MSAP sur l’ensemble d’un cycle de fonctionnement en vue de les optimiser sur cycle. Durant cette thèse, l’auteur a contribué à développer les modèles de couple, de défluxage, de pertes cuivre et de pertes magnétiques et les méthodes de calcul de ces pertes à vide et en charge pour les quatre MSAP dont trois machines à concentration de flux et une machine à aimants en surface du rotor et pour trois cycles de fonctionnement : NEDC, Artemis-Urbain et Artemis-Routier. Une validation expérimentale de ces modèles a été effectuée sur un banc d’essai moteur avec deux prototypes de MSAP. Ensuite, les MSAP ont été dimensionnées en vue d’une minimisation des pertes sur cycle et du courant efficace du point de base. Cette combinaison a pour but d’augmenter le rendement de la machine électrique et de minimiser la dimension de l’onduleur de tension associée. Ce problème d’optimisation multi-objectif a été réalisé en utilisant l'algorithme génétique, Non-Dominated Sorting Genetic Algorithm (NSGA-II). Ainsi, un Front de Pareto des solutions optimales peut être déduit. Les impacts des modèles de pertes (à vide et en charge) sur l’optimisation sur cycle des machines sont étudiés et l’intérêt de chaque modèle est présenté. Les modèles et méthodes de calcul proposés peuvent être appliqués à tous les cycles de fonctionnement, à différentes MSAP et à différentes applications. / Almost all studies of permanent magnet synchronous machines (PMSM) for for hybrid vehicle applications relate to their performances on a specific point of a driving cycle of the vehicle (the base point, the point at high speed or the most used point). However, these machines often operate at different torques and at different speeds. This thesis studies therefore PMSM performances in order to optimize during an entire driving cycle. In this thesis, the author contributed to develop models of torque, field weakening, copper losses and iron losses and methods of calculating these losses at no-load and at load for four MSAP (three concentrated flux machine and a surface mounted PMSM) and for three driving cycles (New Eurepean Driving Cycle, Artemis-Urban and Artemis-Road). An experimental validation of these models was realized on a test bench with two prototypes of MSAP. Then, the MSAP were sized for a minimization of average power losses during the cycle and of the RMS current at the base point. This combination is designed to increase the efficiency of the electrical machine and minimize the size of the associated voltage inverter. This problem of multi-objective optimization was performed using the genetic algorithm, Non-Dominated Sorting Genetic Algorithm (NSGA-II). Thus, a Pareto front of optimal solutions can be derived. The impacts of loss models (at no-load and at load) on the PMSM optimization during the cycle are studied and the interest of each model is presented. Models and calculation methods proposed in this thesis can be applied to all cycles, at different MSAP and for other applications.