The owners of a house in today’s society do not know in real-time how much electricity they use. It could be beneficial for any residential consumer to have more control and overview in real-time over the electricity consumption. This could be done possible with a system that monitors the consumptions, micro renewables and the electricity prices from the grid and then makes a decision to either use or sell electricity to reduce the monthly electricity cost for the household and living a "Greener" life to reduce carbon emissions. In this thesis, estimations are made based on artificial neural network (ANN). The predictions are made for air temperature, solar insolation and wind speed in order to know how much energy will be produced in the next 24 hours from the solar panel and from the wind turbine. The predictions are made for electricity consumption in order to know how much energy the house will consume. These predictions are then used as an input to the system. The system has 3 controls, one to control the amount of sell or buy the energy, one to control the amount of energy to charge or discharge the fixed battery and one to control the amount of energy to charge or discharge the electric vehicle (EV). The output from the system will be the decision for the next 10 minutes for each of the 3 controls. To study the reliability of the ANN estimations, the ANN estimations (SANN) are compared with the real data (Sreal ) and other estimation based on the mean values (Smean) of the previous week. The simulation during a day in January gave that the expenses are 0.6285 € if using SANN, 0.7788 € if using Smean and 0.5974 € if using Sreal. Further, 3 different cases are considered to calculate the savings based on the ANN estimations. The first case is to have the system connected with fixed storage device and EV (Scon;batt ). The second and third cases are to have the system disconnected (without fixed battery) using micro generation (Sdiscon;micro) and not using micro generation (Sdiscon) along with the EV. The savings are calculated as a difference between Scon;batt and Sdiscon, also between Sdiscon;micro and Sdiscon. The saving are 788.68 € during a year if Scon;batt is used and 593.90 € during a year if Sdiscon;micro is used. With the calculated savings and the cost for the equipment, the pay-back period is 15.3 years for Scon;batt and 4.5 years for Sdiscon;micro. It is profitable to only use micro generation, but then the owner of the household loses the opportunity to be part of helping the society to become "Greener".

Hybrid electric vehicles are considered to be an important part of the future vehicle industry, since they decrease fuel consumption without decreasing the performance compared to a conventional vehicle. They use two or more power sources to propel the vehicle, normally one combustion engine and one electric machine. These power sources can be arranged in different topologies and can cooporate in different ways. In this thesis, dynamic models of parallel and series hybrid powertrains are developed, and different strategies for how to control them are compared.An optimization algorithm for decreasing fuel consumption and utilize the battery storage capacity as much as possible is also developed, implemented and tested.

Hybrid electric vehicles have two power sources - an internal combustion engine and an electric motor. These vehicles are of great interest because they contribute to a decreasing fuel consumption and air pollution and still maintain the performance of a conventional car. Different topologies are described in this thesis and especially the series and parallel hybrid electric vehicle and Toyota Prius have been studied. This thesis also depicts modelling of a reference car and a series hybrid electric vehicle in Modelica. When appropriate, models from the Modelica standard library have been used. Models for a manual gearbox, final drive, wheel, chassis, air drag and a driver have been developed for the reference car. For the hybrid electric vehicle a continuously variable transmission, battery, an electric motor, fuel cut-off function for the internal combustion engine and a converter that distributes the current between generator, electric motor and internal combustion engine have been designed. These models have been put together with models from the Modelica standard library to a reference car and a series hybrid electric vehicle which follows the NEDC driving cycle. A sketch for the parallel hybrid electric vehicle and Toyota Prius have also been made in Modelica. Developed models have been introduced into the Modelica library VehProLib, which is a vehicle propulsion library under development by Vehicular Systems, Linköpings universitet.

At the time of this writing, increasing pressure for fuel efficient passenger vehicles has prompted automotive manufactures to invest in the research and development of electrically propelled vehicles. This includes vehicles of strictly electric drive and hybrid electric vehicles with internal combustion engines. To investigate some of the many technological innovations possible with electric power trains, the AUTO21 network of centres of excellence funded project E301-EHV; a project to convert a Chrysler Pacifica into a hybrid electric vehicle. The converted vehicle is intended for use as a test-bed in the research and development of a variety of advances pertaining to electric propulsion. Among these advances is hybrid energy storage, the focus of this investigation. A key difficulty of electric propulsion is the portable storage or provision of electricity, challenges are twofold; (1) achieving sufficient energy capacity for long distance driving and (2) ample power delivery to sustain peak driving demands. Where gasoline is highly energy dense and may be burned at nearly any rate, storing large quantities of electrical energy and supplying it at high rate prove difficult. Furthermore, the demands of regenerative braking require the storage system to undergo frequent current reversals, reducing the service life of some electric storage systems. A given device may be optimized for one of either energy storage or power delivery, at the sacrifice of the other. A hybrid energy storage system (HESS) attempts to address the storage needs of electric vehicles by combining two of the most popular storage technologies; lithium ion batteries, ideal for high energy capacity, and ultracapacitors, ideal for high power discharge and frequent cycles. Two types of HESS are investigated in this study; one using energy-dense lithium ion batteries paired with ultracapacitors and the other using energy-dense lithium ion batteries paired with ultra high powered batteries. These two systems are compared against a control system using only batteries. Three sizes of each system are specified with equal volume in each size. They are compared for energy storage, energy efficiency, vehicle range, mass and relative demand fluctuation when simulated for powering a model Pacifica through each of five different drive cycles. It is shown that both types of HESS reduce vehicle mass and demand fluctuation compared to the control. Both systems have reduced energy efficiency. In spite of this, a battery-battery system increases range with greater storage capacity, but battery-capacitor systems have reduced range. It is suggested that further work be conducted to both optimize the design of the hybrid storage systems, and improve the control scheme allocating power demand across the two energy sources.

The main objective of this research is the development of a flexible test-bench for evaluation of hybrid electric powertrain controllers. As a case study, a real-time near-optimal powertrain controller for a series hybrid electric vehicle (HEV) has been designed and tests. The designed controller, like many other optimal controllers, is based on a simple model. This control-oriented model aims to be as simple as possible in order to minimize the controller computational effort. However, a simple model may not be able to capture the vehicle's dynamics accurately, and the designed controller may fail to deliver the anticipated behavior. Therefore, it is crucial that the controller be tested in a realistic environment. To evaluate the performance of the designed model-based controller, it is first applied to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. After successfully passing this model-in-the-loop test, the controller is programmed into a rapid-prototyping controller unit for hardware-in-the-loop simulations. This type of simulation is mostly intended to consider controller computational resources, as well as the communication issues between the controller and the plant (model solver). As the battery pack is one of the most critical components in a hybrid electric powertrain, the component-in-the-loop simulation setup is used to include a physical battery in the simulations in order to further enhance simulation accuracy. Finally, the driver-in-the-loop setup enables us to receive the inputs from a human driver instead of a fixed drive cycle, which allows us to study the effects of the unpredictable driver behavior. The developed powertrain controller itself is a real-time, drive cycle-independent controller for a series HEV, and is designed using a control-oriented model and Pontryagin's Minimum Principle. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions; however, the amount of information is reduced. Although the controller design procedure is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to obtain the supervisory controller for a series HEV with an ultra-capacitor. By testing the designed optimal controller with the prescribed simulation setups, it is shown that the controller can ensure optimal behavior of the powertrain, as the dominant system behavior is very close to what is being predicted by the control-oriented model. It is also shown that the controller is able to handle small uncertainties in the driver behavior.

A plug-in hybrid electric vehicle (PHEV) is a vehicle powered by a combination of an internal combustion engine and an electric motor with a battery pack. The battery pack can be charged by plugging the vehicle into the electric grid or from using excess engine power. A PHEV allows for all electric operation for limited distances, while having the operation and range of a conventional hybrid electric vehicle on longer trips. A PHEV design with design parameters electric motor size, engine size, battery capacity, and battery chemistry type, is optimized with minimum cost as a figure of merit. The PHEV is required to meet a fixed set of performance constraints consisting of 0-60 mph acceleration, 50-70 mph acceleration, 0-30 mph acceleration in all electric operation, top speed, grade ability, and all electric range. The optimization is carried out for values of all electric range of 10, 20, and 40 miles. The social and economic impacts of the optimum designs in terms of reduced gasoline consumption and carbon emissions reduction are calculated. Argonne National Laboratorys Powertrain Systems Analysis Toolkit is used to simulate the performance and fuel economy of the PHEV designs. The costs of different PHEV components and the present value of battery replacements over the vehicles life are used to determine the designs drivetrain cost. The resulting optimum PHEVs are designs using lead acid battery type. The optimum design parameter values are all determined by a single controlling performance constraint. The PHEV designs show a 63% to 80% reduction in gasoline consumption and a 53% to 47% reduction in CO2 emissions. The PHEV designs have an annual gas savings of $696 to $643 per year over the average sedan meeting the 27.5 mpg CAFE standards.

In this paper, the development and control of a switched reluctance motor (SRM) applied to the intelligent electric vehicle are presented. In the SRM control policy, speed control and constant torque/constant power control are implemented with modified PI(RISC) control and a chopped current control(CCC). The control policy can restrain torque ripple effectively, and the vibration and acoustic noise are reduced involuntarily. Simultaneously, The speed response to sudden load change becomes less dramatic. Simulate results suggest that modified PI is more powerful than fuzzy control and PI. In the experiment, this paper compares the advantages and disadvantages of PI, fuzzy control and modified PI under no load, fixed load and unfixed load condition. Finally, a Digital Signal Processor(DSP)is adopted to verify the accuracy of simulation, which contributes to the planning of the program composition flow.

The automobile industry is moving fast towards Electric Vehicles (EV); however this paradigm shift is currently making its smooth transition through the phase of Hybrid Electric Vehicles. There is an ever-growing need for integration of hybrid energy sources especially for vehicular applications. Different energy sources such as batteries, ultra-capacitors, fuel cells etc. are available. Usage of these varied energy sources alone or together in different combinations in automobiles requires advanced power electronic circuits and control methodologies. An exhaustive literature survey has been carried out to study the power electronic converter, switching modulation strategy to be employed and the particular machine to be used in an EV. Adequate amount of effort has been put into designing the vehicle specifications. Owing to stronger demand for higher performance and torque response in an EV, the Permanent Magnet Synchronous Machine has been favored over the traditional Induction Machine. The aim of this thesis is to demonstrate the use of a multi level inverter fed Brush Less Direct Current (BLDC) motor in a field oriented control fashion in an EV and make it follow a given drive cycle. The switching operation and control of a multi level inverter for specific power level and desired performance characteristics is investigated. The EV has been designed from scratch taking into consideration the various factors such as mass, coefficients of aerodynamic drag and air friction, tire radius etc. The design parameters are meant to meet the requirements of a commercial car. The various advantages of a multi level inverter fed PMSM have been demonstrated and an exhaustive performance evaluation has been done. The investigation is done by testing the designed system on a standard drive cycle, New York urban driving cycle. This highly transient driving cycle is particularly used because it provides rapidly changing acceleration and deceleration curves. Furthermore, the evaluation of the system under fault conditions is also done. It is demonstrated that the system is stable and has a ride-through capability under different fault conditions. The simulations have been carried out in MATLAB and Simulink, while some preliminary studies involving switching losses of the converter were done in PSIM.

In the first part of this thesis, GPS data for a year's worth of travel by 255 Seattle households is used to illuminate how plug-in electric vehicles (PEVs) can match household needs. Data from all vehicles in each of these households were analyzed at a disaggregate level primarily to determine whether each household would be able to adopt various types of PEVs without significant issues in meeting travel needs. The results suggest that a battery-electric vehicle (BEV) with 100 miles of all-electric range (AER) should meet the needs of 50% of Seattle's one-vehicle households and the needs of 80% of the multiple-vehicle households, when households charge just once a day and rely on another vehicle or mode just 4 days a year. Moreover, the average one-vehicle Seattle household uses each vehicle 23 miles per day and should be able to electrify close to 80% of its miles, while meeting all its travel needs, using a plug-in hybrid electric vehicle with 40-mile all-electric-range (PHEV40). Households owning two or more vehicles can electrify 50 to 70% of their total household miles using a PHEV40, depending on how they assign the vehicle across drivers each day. Cost comparisons between the average single-vehicle household owning a Chevrolet Cruze versus a Volt PHEV suggest that, when gas prices are $3.50 per gallon and electricity rates are 11.2 ct per kWh, the Volt will save the household $535 per year in energy/fuel costs. Similarly, the Toyota Prius PHEV will provide an annual savings of $538 per year over the Corolla. The results developed in this research provide valuable insights into the role of AER on PEV adoption feasibility and operating cost differences. The second part of this thesis uses detailed travel data from the Seattle metropolitan area to evaluate the effects of built-environment variables on the use of non-motorized (bike + walk) modes of transport. Several model specifications are used to understand and explain non-motorized travel behavior in terms of household, person and built-environment variables. Land-use measures like land-use mix, density, and accessibility indices were also created and incorporated as covariates to appreciate their marginal effects. The models include a count model for household vehicle ownership levels, a binary choice model for the decision to stay within versus departing one's origin zone (i.e., intra- versus inter-zonal trip-making), discrete choice models for destination choices and mode choices, and a zero-inflated negative binomial model for non-motorized trip counts per household. The mode and destination choice models were estimated separately for interzonal and intrazonal trips and for each of three different trip types (home-based work, home-based non-work, and non-home-based), to recognize the distinct behaviors at play when making shorter versus longer trips and different types of trips. This comprehensive set of models highlights how built-environment variables -- like the number and type of intersections present around one's origin and destination, the number of bus stops available within a certain radius, household and jobs densities, parking prices, land use mixing, and walk-based accessibility -- can significantly shape the pattern of one's non-motorized movement. The results underscore the importance of street connectivity (quantified as the number of 3-way and 4-way intersections in a half-mile radius), higher bus stop density, and greater non-motorized access in promoting lower vehicle ownership levels (after controlling for household size, income, neighborhood density and so forth), higher rates of non-motorized trip generation (per day), and higher likelihoods of non-motorized mode choices. Destination choices are also important for mode choices, and local trips lend themselves to more non-motorized options than more distance trips. Intrazonal trip likelihoods rose with higher street connectivity, transit availability, and land use mixing. For example, the results suggest that an increase in the land-use mix index by 10% would increase the probability of choosing to travel within the zone by 12%. As expected destinations with greater population and job numbers (attraction), located closer (to a trip's origin), offering lower parking prices and greater transit availability, were more popular. Interestingly, those with more dead ends (or cul de sacs) attracted fewer trips. Among all built environment variables tested, street structure offered the greatest predictive benefits, alongside jobs and population (densities and counts). For example, a 1-percent increase in the average number of 4-way intersections within a quarter-mile radius of the sampled households is estimated to increase the average household's non-motorized trip generation by 0.36%. A one-standard-deviation increase in the (mean) number of 4-way intersections at the average trip origin is estimated to increase the probabilities of bike and walk modes for interzonal home-based-work trips by 57% and 30%, respectively. In contrast, increasing the number of dead-ends at the origin by one standard deviation is estimated to decrease the probability of biking for both home-based-work and non-work trips by ~30%. These results underscore the importance of network density and connectivity for promoting non-motorized activity. The regional non-motorized travel (NMT) accessibility index ( derived from the logsum of a destination choice model) also offers strong predictive value, with NMT counts rising by by 7% following a 1% increase in this variable -- if the drive alone accessibility index is held constant (along with all other variables, evaluated at their means). Similarly, household vehicle ownership is expected to fall by 0.36% with each percentage point increase in the NMT accessibility index, and walk probabilities rise by 26.9% following a one standard deviation increase in this index at the destination zone. A traveler's socio-economic attributes also have important impacts on NMT choices, with demographics typically serving as much stronger predictors of NMT choices than the built environment. For example, the elasticity of NMT trip generation with respect to a household's vehicle ownership count is estimated to be -0.52. Males and tose with drivers licenses are estimated to have 17% and 39% lower probabilities, respectively, of staying within their origin zone, relative to women and unlicensed adults (ceteris paribus). Non-motorized model choices also exhibit strong sensitivity to age and gender settings. Several of the regional variables developed in this work, and then used in the predictive models, are highly correlated. For example, bus stop and intersection densities are very high in job- and population-dense areas. For example, the correlation co-efficients between the bus stop density and 4-way intersection density is 0.805, between NMT and SOV AIs is 0.830 and between 4-way intersection density and NMT AI is 0.627. As a result, many variables are proxying for and/or competing with each other, as is common in models with many land use covariates, and it is difficult to quantify the exact impact of each of these variables. Nonetheless the models developed here provide valuable insight into the role of several new variables on non-motorized travel choices. Some final case study applications, moving all households to the downtown area (that has high accessibility indices and density), illustrate to what extent these revealed-data-based models will predict shifts toward and away from non-motorized trip-making. It appears that average household vehicle ownership level reduces to 0.57 from 1.89 (a 70% reduction) and average two-day NMT trip generation increases to 5.92 from 0.83 (an increase of more than 6 times). Such ranges are valuable to have in mind, when communities seek to reduce reliance on motorized travel by defining new built-environment contexts. / text

There has been a significant increase in private vehicle ownership in the last decade leading to substantial increase in air pollution, depleting fuel reserves, etc. One of the alternatives known as battery operated electric vehicles (BEVs) has the potential to reduce carbon footprints due to lesser or no emissions and thus the focus on shifting people from gasoline operated vehicles (GVs) to BEVs has increased considerably recently. However, BEVs have a limited ‘range’ and takes considerable time to completely recharge its battery. In addition, charging stations are not as pervasive as gasoline stations. As a result a new fear of getting stranded is observed in BEV drivers, known as range anxiety. Range anxiety has the potential to substantially affect the route choice of a BEV user. It has also been a major cause of lower market shares of BEVs. Range anxiety is a latent feeling which cannot be measured directly. It is not homogenous either and varies among different socio-economic groups. Thus, a better understanding of BEV users’ behavior may shed light on some potential solutions that can then be used to improve their market shares and help in developing new network models which can realistically capture effects of varying EV adoptions. Thus, in this study, we analyze the factors that may impact BEV users’ range anxiety in addition to their route choice behavior using the integrated choice latent variable model (ICLV) proposed by Bhat and Dubey (2014). Our results indicate that an individual’s range anxiety is significantly affected by their age, gender, income, awareness of charging stations, BEV ownership and other category vehicle ownership. Further, it also highlights the importance of including disutility caused by distance while considering network flow models with combined GV and BEV assignment. Finally, a more concentrated effort can be directed towards increasing the awareness of charging station locations in the neighborhood to help reduce the psychological barrier associated with range anxiety. Overcoming this barrier may help increase consumer confidence, resulting in increased BEV adoption and ultimately will lead towards a potentially pollution-free environment. / text