New SR drive with integrated charging capacity for plug-in hybrid electric vehicles (PHEVs)

Plug-in hybrid electric vehicles (PHEVs) provide much promise in reducing greenhouse gas emissions and, thus, are a focal point of research and development. Existing on-board charging capacity is effective but requires the use of several power conversion devices and power converters, which reduce reliability and cost efficiency. This paper presents a novel three-phase switched reluctance (SR) motor drive with integrated charging functions (including internal combustion engine and grid charging). The electrical energy flow within the drivetrain is controlled by a power electronic converter with less power switching devices and magnetic devices. It allows the desired energy conversion between the engine generator, the battery, and the SR motor under different operation modes. Battery-charging techniques are developed to operate under both motor-driving mode and standstill-charging mode. During the magnetization mode, the machine's phase windings are energized by the dc-link voltage. The power converter and the machine phase windings are controlled with a three-phase relay to enable the use of the ac-dc rectifier. The power converter can work as a buck-boost-type or a buck-type dc-dc converter for charging the battery. Simulation results in MATLAB/Simulink and experiments on a 3-kW SR motor validate the effectiveness of the proposed technologies, which may have significant economic implications and improve the PHEVs' market acceptance

Impact of electric vehicles on a carbon constrained power system—a post 2020 case study

Electric vehicles (EVs) offer great potential to move from fossil fuel dependency in transport once some of the technical barriers related to battery reliability and grid integration are resolved. The European Union has set a target to achieve a 10% reduction in greenhouse gas emissions by 2020 relative to 2005 levels. This target is binding in all the European Union member states. If electric vehicle issues are overcome then the challenge is to use as much renewable energy as possible to achieve this target. In this paper, the impacts of electric vehicle charged in the all-Ireland single wholesale electricity market after the 2020 deadline passes is investigated using a power system dispatch model. For the purpose of this work it is assumed that a 10% electric vehicle target in the Republic of Ireland is not achieved, but instead 8% is reached by 2025 considering the slow market uptake of electric vehicles. Our experimental study shows that the increasing penetration of EVs could contribute to approach the target of the EU and Ireland government on emissions reduction, regardless of different charging scenarios. Furthermore, among various charging scenarios, the off-peak charging is the best approach, contributing 2.07% to the target of 10% reduction of Greenhouse gas emissions by 2025.

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Analysis of electric vehicle charging using the traditional generation expansion planning analysis tool WASP-IV

Electric vehicles (EV) are proposed as a measure to reduce greenhouse gas emissions in transport and support increased wind power penetration across modern power systems. Optimal benefits can only be achieved, if EVs are deployed effectively, so that the exhaust emissions are not substituted by additional emissions in the electricity sector, which can be implemented using Smart Grid controls. This research presents the results of an EV roll-out in the all island grid (AIG) in Ireland using the long term generation expansion planning model called the Wien Automatic System Planning IV (WASP-IV) tool to measure carbon dioxide emissions and changes in total energy. The model incorporates all generators and operational requirements while meeting environmental emissions, fuel availability and generator operational and maintenance constraints to optimize economic dispatch and unit commitment power dispatch. In the study three distinct scenarios are investigated base case, peak and off-peak charging to simulate the impacts of EV’s in the AIG up to 2025.

A well-to-wheel analysis of electric vehicles in the all-island Single Electricity Market

The European Union has set a target for 10% renewable energy in transport by 2020 to be met using biofuels and electric vehicles. In the case of biofuels, the biofuel must achieve greenhouse gas savings of 35% relative to the fossil fuel replaced. For biofuels, greenhouse gas savings can be calculated using life cycle analysis or the European Union default values. In contrast, all electricity used in transport is considered to be the same, regardless of the source or the type of electric vehicle. However, the choice of the electric vehicle and electricity source will have a major impact on the greenhouse gas saving. In this paper the initial findings of a well-to-wheel analysis of electric vehicle deployment in Northern Ireland are presented. The key finding indicates that electric vehicles require least amount of energy per mile on a well-to-wheel basis, consume the fewest resources, even accommodating inefficient fuel production, in comparison to standard internal combustion engine and hybrid vehicles.

Investigation into the potential of using micro gas turbines in plug-in hybrid electric vehicles

The transport sector is considered to be one of the most dependent sectors on fossil fuels. Meeting ecological, social and economic demands throughout the sector has got increasingly important in recent times. A passenger vehicle with a more environmentally friendly propulsion system is the hybrid electric vehicle. Combining an internal combustion engine and an electric motor offers the potential to reduce carbon dioxide emissions. The overall objective of this research is to provide an appraisal of the use of a micro gas turbine as the range extender in a plug-in hybrid electric vehicle. In this application, the gas turbine can always operate at its most efficient operating point as its only requirement is to recharge the battery. For this reason, it is highly suitable for this purpose. Gas turbines offer many benefits over traditional internal combustion engines which are traditionally used in this application. They offer a high power-to-weight ratio, multi-fuel capability and relatively low emission levels due to continuous combustion.

A techno-economic evaluation of changing from conventional vehicles to range extended electric vehicles

Transportation accounts for 22% of greenhouse gas emissions in the UK, and increases to 25% in Northern Ireland. Surface transport carbon dioxide emissions, consisting of road and rail, are dominated by cars. Demand for mobility is rising rapidly and vehicle numbers are expected to more than double by 2050. Car manufacturers are working towards reducing their carbon footprint through improving fuel efficiency and controlling exhaust emissions. Fuel efficiency is now a key consideration of consumers purchasing a new vehicle. While measures have been taken to help to reduce pollutants, in the future, alternative technologies will have to be used in the transportation industry to achieve sustainability. There are currently many alternatives to the market leader, the internal combustion engine. These alternatives include hydrogen fuel cell vehicles and electric vehicles, a term which is widely used to cover battery electric vehicles, plug-in hybrid electric vehicles and extended-range electric vehicles. This study draws direct comparisons measuring the differing performance in terms of fuel consumption, carbon emissions and range of a typical family saloon car using different fuel types. These comparisons will then be analysed to see what effect switching from a conventionally fuelled vehicle to a range extended electric vehicle would have not only on the end user, but also the UK government.

Electric vehicles and opportunities to displace tailgate emissions using Smart Charging controls

Under the European Union Renewable Energy Directive each Member State is mandated to ensure that 10% of transport energy (excluding aviation and marine transport) comes from renewable sources by 2020. The Irish Government intends to achieve this target with a number of policies including ensuring that 10% of all vehicles in the transport fleet are powered by electricity by 2020. This paper investigates the impact of the 10% electric vehicle target in Ireland in 2020 using a dynamic programming based long term generation expansion planning model. The model developed optimizes power dispatch using hourly electricity demand curves up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. Two distinct scenarios are analysed based on a peak and off-peak charging regimes in order to simulate the effects of the electric vehicles charging in 2020. The importance and influence of the charging regimes on the amount of energy used and tailgate emissions displaced is then determined.

Electric vehicle capacity forecasting model with application to load levelling

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Energy levels and radiative rates for transitions in Ti VII

We report calculations of energy levels, radiative rates, oscillator strengths and line strengths for transitions among the lowest 231 levels of Ti VII. The general-purpose relativistic atomic structure package and flexible atomic code are adopted for the calculations. Radiative rates, oscillator strengths and line strengths are provided for all electric dipole (E1), magnetic dipole (M1), electric quadrupole (E2) and magnetic quadrupole (M2) transitions among the 231 levels, although calculations have been performed for a much larger number of levels (159 162). In addition, lifetimes for all 231 levels are listed. Comparisons are made with existing results and the accuracy of the data is assessed. In particular, the most recent calculations reported by Singh et al (2012 Can. J. Phys. 90 833) are found to be unreliable, with discrepancies for energy levels of up to 1 Ryd and for radiative rates of up to five orders of magnitude for several transitions, particularly the weaker ones. Based on several comparisons among a variety of calculations with two independent codes, as well as with the earlier results, our listed energy levels are estimated to be accurate to better than 1% (within 0.1 Ryd), whereas results for radiative rates and other related parameters should be accurate to better than 20%.

Energy levels and radiative rates for transitions in Ti VI

We report on calculations of energy levels, radiative rates, oscillator strengths and line strengths for transitions among the lowest 253 levels of the (1s22s22p6 ) 3s23p5 , 3s3p6 , 3s23p43d, 3s3p53d, 3s23p33d2 , 3s23p44s, 3s23p44p and 3s23p44d configurations of Ti VI. The general-purpose relativistic atomic structure package and flexible atomic code are adopted for the calculations. Radiative rates, oscillator strengths and line strengths are reported for all electric dipole (E1), magnetic dipole (M1), electric quadrupole (E2) and magnetic quadrupole (M2) transitions among the 253 levels, although calculations have been performed for a much larger number of levels. Comparisons are made with existing available results and the accuracy of the data is assessed. Additionally, lifetimes for all 253 levels are listed, although comparisons with other theoretical results are limited to only 88 levels. Our energy levels are estimated to be accurate to better than 1% (within 0.03 Ryd), whereas results for other parameters are probably accurate to better than 20%. A reassessment of the energy level data on the National Institute of Standards and Technology website for Ti VI is suggested.

Energy levels and radiative rates for transitions in Ti X

We report calculations of energy levels, radiative rates, oscillator strengths and line strengths for transitions among the lowest 345 levels of Ti X. These include 146 levels of the n 3 configurations and 86 of 3s ²4ℓ, 3s²5ℓ and 3s3p4ℓ, plus some of the 3s²6ℓ, 3p²4ℓ and 3s3p5ℓ levels. The general-purpose relativistic atomic structure package and flexible atomic code are adopted for the calculations. Radiative rates, oscillator strengths and line strengths are provided for all electric dipole (E1), magnetic dipole (M1), electric quadrupole (E2) and magnetic quadrupole (M2) transitions among the 345 levels, although calculations have been performed for a much larger number of levels. Comparisons are made with existing results and the accuracy of the data is assessed. Additionally, lifetimes for all 345 levels are listed. Extensive comparisons of lifetimes are made for the lowest 40 levels, for which discrepancies with recent theoretical work are up to 30%. Discrepancies in lifetimes are even larger, up to a factor of four, for higher excited levels. Furthermore, the effect of large configuration interaction (CI) is found to be insignificant for both the energies and lifetimes for the lowest 40 levels of Ti X which belong to the 3s²3p, 3s3p², 3s²3d, 3p³ and 3s3p3d configurations. However, the contribution of CI is more appreciable for the energy levels and radiative rates among higher excited levels. Our listed energy levels are estimated to be accurate to better than 1% (within 0.1 Ryd), whereas results for other parameters are probably accurate to better than 20%. © 2013 The Royal Swedish Academy of Sciences.

Energy levels, radiative rates and electron impact excitation rates for transitions in Fe XIV

Energies and lifetimes are reported for the lowest 136 levels of Fe XIV, belonging to the (1s(2)2s(2)2p(6)) 3s(2)3p, 3s(3)p(2), 3s(2)3d, 3p(3), 3s(3)p(3)d, 3p(2)3d, 3s(3)d(2), 3p(3)d(2) and 3s(2)4l configurations. Additionally, radiative rates, oscillator strengths and line strengths are calculated for all electric dipole (E1), magnetic dipole (M1), electric quadrupole (E2) and magnetic quadrupole (M2) transitions. Theoretical lifetimes determined from these radiative rates for most levels show satisfactory agreement with earlier calculations, a swell as with measurements. Electron impact excitation collision strengths are also calculated with the Dirac atomic R-matrix code (DARC) over a wide energy range up to 260 Ryd. Furthermore, resonances have been resolved in a fine energy mesh to determine effective collision strengths, obtained after integrating the collision strengths over a Maxwellian distribution of electron velocities. Results are listed for all 9180 transitions among the 136 levels over a wide range of electron temperatures, up to 10(7.1) K. Comparisons are made with available results in the literature, and the accuracy of the present data is assessed.