This thesis focuses on developing new scheduling strategies for the integration of plug-in electric vehicles from power system scheduling perspectives. Economic and environmental load dispatch and unit commitment problems are combined with fixed load profiles as well as intelligent scheduling of plug-in electric vehicles charging and discharging scenarios. In this thesis categories of electric vehicles and the potential scheduling capacity of plug-in electric vehicles are first addressed. Then the state-of-the-art scheduling methods to integrate plug-in electric vehicles are surveyed, examined and categorised based on their computational techniques. The preliminaries of mete-heuristic algorithms preliminary including continuous and discrete methods which would be adopted in the scheduling strategies development. Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimising fossil fuel costs and air pollution emissions subject to operational and licensing requirements. It is of significant importance to achieve the optimal result for the economic and environmental load dispatch considering the impact of plug-in electric vehicles. Therefore, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environmental dispatch model. A novel self-learning teaching-learning based optimisation is proposed to solve the non-convex non-linear dispatch problems. To simultaneously solve the unit commitment and hour based scheduling problem of the plug-in electric vehicles aggregators, a novel hybrid mixed coding meta-heuristic algorithm is proposed, combining five variants of binary symmetric particle swarm optimisation with various transfer functions, a real valued self-adaptive differential evolution and a lambda iteration method. The impact of the transfer function utilised in binary optimisation to solve the unit commitment and plug-in electric vehicle integration is investigated in a 10 unit power system with 50,000 plug-in electric vehicles.

Environmental concern and the opportunity for commercial gain are two factors driving the expansion of the electric vehicle (EV) market. Due to the limitations of current battery technology, the efficiency of the traction drive, which includes the electric motor and power electronic converter, is of prime importance. Whilst electric machines utilising permanent magnets (PMs) are popular due to their high energy density, industry concerns about the security of supply have led to interest in magnet-free solutions. Induction machines (IMs) offer such an option. Control of IMs is a mature but complex field. Many techniques for optimising the efficiency of the drive system have been proposed. The vast majority of these methods involve an analytical study of the system to reveal relationships between the controlled variable and efficiency, allowing the latter to be optimised. This inevitably involves simplifications of the problem to arrive at a practically-implementable control scheme. What has not been investigated is real-time calculation of the system losses in order to optimise the efficiency, and the work presented in this thesis attempts to achieve this. The conventional control scheme is examined and a new structure implemented where a model of the system loss is able to directly influence the switching action of the inverter, thus reducing loss. The need to maintain performance alongside loss minimisation is recognised and a cost function-based solution proposed. The validation of this structure is performed both in simulation and on a practical test platform. A model of the principle losses in the drive system is derived, taking into account the processing power typically available for this application, and implemented in the structure outlined. The effect of the new control scheme on efficiency is investigated and results show gains of up to 3%-points are achievable under certain conditions.

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.

Electrification of transport forms a major part of British policy for energy and climate change. The formation of the early market for Electric Vehicles (EVs) has been supported through consumer subsidies, regulatory support, and programmes for the deployment of electric vehicle charging infrastructure, but uptake does not seem to be proceeding at the rate needed for meeting policy objectives. The approach pursued by policy actors is consistent with the approach of Strategic Niche Management (SNM), which would call for the creation of protected spaces to facilitate the development of new sociotechnical configurations. The Plugged-in Programme (PiP) in Milton Keynes is an example of creating a protective space. A comparison of PiP and other case studies in the literature of sociotechnical transitions identified a gap in SNM that may shed light on the limitations of EV policy in the UK. Traditionally, SNM has been used to monitor and manage interventions in support of prototype or pre-production vehicles. In consequence, there is no precedent for its application in support of early market technologies. The market introduction of innovative technologies can trigger interrelated technological and behavioural changes, affecting the preferences of producers and consumers while altering the demand structure of the sector. However, SNM does not account for the patterns of use and demand implied in what remain largely technological templates for the future. This thesis begins to develop a framework for the analysis and management of early market strategic niches. Insights from a second discipline, that of social marketing, were sought to complement the analytical tools of SNM. Social marketing is useful for understanding the effect of behavioural and market factors on the adoption of innovative technologies. Social marketing provides a framework for analysing and influencing behaviour in socially beneficial directions. Behaviour and choice are modulated through the application of a marketing orientation, identifying and addressing needs and creating valuable offerings. This research is centred on organizational users of electric vehicles (EVs), and explores the effectiveness of the policy portfolio for addressing the needs of early adopters and for building an early market for EVs. Thematic analysis, a form of qualitative content analysis, is applied to evidence from documentary sources, participant observation and interviews with key organizational actors in the community of pioneering and prospective EV users. The analysis draws on concepts from SNM and social marketing to explore previously neglected forces affecting the early market for EVs, with particular focus on the increasing importance of market selection and the competition presented by an entrenched but socially undesirable incumbent. Contrary to the expectations of policy actors, financial incentives and infrastructure deployment have a limited impact on the choices made by organizational actors. This thesis shows that the processes of learning and embedding that take place within the niche need to be multidimensional. Before a choice can be made, pioneering and prospective adopters of EVs invest considerable effort in the collaborative construction of new patterns of use and demand. This process can be supported by empowering interventions that identify suitable applications (creating multiple sub-niches within the niche) and facilitate the co-construction of new, competitive configurations around them. The models and networks created through this multidimensional, collaborative process translate into capabilities that give distinct advantages to pioneering adopters, allowing them to expand beyond their original niche and outperform the incumbents in mainstream markets.

An anticipated increase in the number of electric vehicles (EVs) on the road has created the need to understand and manage recharging demand in order to prevent localised overloading of power distribution networks during peak hours. Smart meters at home, in conjunction with off-peak energy tariffs, have been proposed as a demand management tool. This has not been tested in a region with a high density recharging infrastructure whereby drivers pay an annual fixed fee for unlimited use of non-domestic recharging infrastructure networks. This research quantified daily recharging demand profiles and assessed the effectiveness of incentivising off-peak recharging in such a region. The North East of England was used as the study area. Between 2010 and 2013, 401 home, 312 workplace and 412 public non-domestic recharging posts were installed. Recharging data were available from SwitchEV; a three year, real world EV deployment study that commenced in 2010. Sources of data were in-vehicle loggers, focus groups and questionnaires. There were 23 Private, 43 Organisation Individual users and 74 Organisation Pool users in total. Five statistically significantly different representative recharging profiles were identified. None of these profiles had high demand peaks during the off-peak hours between midnight and 07:00hrs. Interventions took place for 21 users. A 50% reimbursement for off-peak recharging was offered. At home, off-peak recharging increased by 23%. No significant changes in recharging behaviour occurred at any other recharging location. There was also no statistically significant change in the proportion of total recharging recorded at each location. Focus groups and questionnaires revealed the common theme of drivers using EV recharging posts as they offer free and convenient parking bays, rather than out of a need to recharge the battery in order to complete an upcoming trip. Furthermore, the absence of timing devices and organisation policy dictating that EVs must be recharged immediately upon returning to the premises limited the ability of organisations to deliver behavioural change. It is recommended that pay-as-you-go access to non-domestic recharging infrastructure be implemented to reduce unnecessary daytime recharging and that workplace recharging infrastructure is fitted with smart meters. These changes are required as this research has highlighted limitations of the current proposed policy.

This thesis deals with the requirement to improve the overall fuel efficiency of a vehicle, through the optimisation of transmission layout and the number of gears. This development leads on to research surrounding the specification of the hybrid electric transmission system that is best suited to the case study vehicle. There are many existing papers discussing the energy efficiency and fuel consumption reduction potential of hybrid electric vehicles. However there is comparatively less research concerning the selection procedure of the initial basic mechanical layout of the transmission that is hybridised, which this PhD project addresses. Initially a simulation model of the case study vehicle transmission was created to assess the individual power loss contributions within the transmission, and quantify their individual impact on the overall powertrain efficiency during different driving cycles. The simulation model was validated against experimental test results collected on a vehicle rolling road. Following this, the number of selectable gears influence on the overall powertrain efficiency was analysed. With these findings a fuel consumption improvement of 7.2% for the New European Driving Cycle (NEDC) over the case study powertrain was realised, through the implementation of the improved transmission mechanical specification. A hybrid electric transmission architecture based on the aforementioned transmission mechanical specification was created. This provided an improvement in fuel efficiency over the baseline internal combustion engine driven vehicle, as well as deliver comparable driver comfort during gear shifts through the adoption of electric motor torque filling. Finally an optimal power split strategy for the hybrid electric vehicle was developed, including optimal gear selection. This optimal power split strategy coupled with the improved transmission mechanical specification provides a fuel consumption improvement of 21% over the baseline 7-speed dual clutch transmission (DCT) during the NEDC. Experimental transmission testing for both the baseline DCT and the hybrid electric transmission, OGeco, has been carried out on the University of Surrey HIL transmission test rig. The findings from the transmission efficiency test results correlate with the predicted results found through simulation.

The penetration of the electric vehicle (EV) has increased rapidly in recent years mainly as a consequence of advances in both transportation and electricity sectors and in response to global pressure to reduce carbon emissions and limit fossil fuel consumption. Large-scale EV integration in power systems has modified the nature of the traditional electric load such that it should be controllable. Moreover, uncertain power sources and demand pose challenges in electricity transmission grid, leading to significant impact on power system security and stability. Therefore, it is timely that a comprehensive study of the impacts of large-scale EVs integration on power system stability is published. This thesis introduces EV development and typical global research and examines stochastic and intermittent issues which have parameterised in time, location, and magnitude. The work initially develops a flexible EV charging and discharging capacity forecasting model, which is suitable for different kinds of optimisation objects. Based on the proposed model, the main body of this work examines steady-state and transient stability analysis. In steady-state analysis, EV station siting and sizing and steady-state stability are considered. In transient stability analysis, an AC/DC converter-based EV station model has been proposed. EV connections and typical faults are discussed. Critical clearing time (CCT) and transient stability margin are used to assess transient stability by time-domain simulation. Two further topics, using local battery energy storage to meet local demand and application of an EV module for power system dispatch have been proposed as complementary applications for distribution networks and transmission grids.

Electric vehicles (EVs) are rapiding gaining popularity worldwide in recent years as a way of replacing the internal combustion engine vehicles to improve fuel efficiency and to reduce emissions in the transport sector. Lithium ion batteries have been widely used in EVs as the power source due to their several advantages, such as high energy/power density, long service life, high efficiency and environmentally friendly features. A battery management system (BMS) is essential in EV applications for safe and efficient operation of the battery pack where hundreds or even thousands of battery cells are connected in series/parallel configuration to fulfil the high power and high voltage needs of the vehicles. This thesis is focused on battery modelling, internal state estimation and control algorithms for BMS applications. In this thesis, a lithium ion battery is firstly characterized experimentally with different load profiles and at different temperature levels using a battery test system. After collecting test data and conducting literature survey, a novel simplified battery thermoelectric model is proposed, which includes an electrical submodel and a thermal submodel. The couplings between battery thermal and electrical behaviours are also captured. A novel hybrid parameter optimization method is proposed for model training by comining the least squares method and a meta-heuristic optimization algorithm. Based on the developed battery model, battery internal state estimation is then studied, such as state of charge and internal temperature, using the extended Kalman filter method. Finally, the proposed battery model and internal state estimation methods are used to develop battery management strategies, in particular for real-time battery thermal management control algorithms. Different controllers are proposed and compared, e.g., PID controller, bang-bang controller and optimal controller, in order to achieve optimal battery thermal control performance.

Public awareness of finite oil resources and concerns over climate change have spurred efforts to improve vehicle efficiency and reduce emissions by road transport. Hybrids have become an increasingly popular alternative to conventional powertrain vehicles. Large fuel savings are claimed (typically 70 + mpg) (Toyota, 2014), however, collective anecdotal evidence from owners of these vehicles suggests a more modest performance. A literature review yielded an abundance of literature relating to specific hybrid vehicle technologies, and control strategies, however the variation in energy savings over different journey types for different classes of vehicle has received less attention. A simulation tool was developed to compare the energy saving effectiveness of parallel hybrid powertrains with regenerative braking and energy storage across a broad range of vehicle and journey types. The realism of the simulation (in non-hybrid mode) was evaluated by comparison with practical trials. A range of validation methods showed that average fuel consumption could be calculated to within +/- 5-10% of measured consumption and, in cases where detailed data for a vehicle was available, this improved to within 3%. Simulated fuel consumption was around 15% greater that manufacturers’ claims – reasons for this were explored. Using the backward and forward looking simulation it was possible to calculate likely fuel savings in various scenarios. Results indicate a trend of improved potential savings with increased vehicle mass. Over urban journeys results ranged from around 16 to 23% energy savings for a small car and large coach respectively. On extra-urban journeys much more modest savings were calculated ranging from a maximum of 0 - 4 % across the same range of vehicles. The likely effects of vehicle mass and drag coefficient has also been explored along with the energy saving potential of start-stop engine technology, often used in hybrids and non-hybrids alike. The broad part of the study confirmed quantitatively that greatest fuel savings might be achieved on urban routes with public transport buses. The study then narrowed to consider this application, particularly with respect to exhaust emissions which are cause for growing concern. Possible reductions in exhaust NOx and PM emissions of up to 10 to 12% respectively were predicted through the application of parallel hybrid powertrains to existing bus designs and simulated on the MLTB cycle.

Given the recent interest in the replacement of internal combustion engines with electric machines, the main aim of this research is to design a high torque density permanent magnet machine to an electric vehicle’s specification. A new design concept for increasing the torque density of a conventional permanent magnet machine is introduced and described theoretically and mathematically. The proposed method involves splitting the stator teeth in order to introduce a magnetic gear ratio into the torque equation. The introduced machine is also further modified to improve its magnet utilisation. An investigation is carried out on the impact of different slot to pole combinations on torque density and an optimum combination found. Thereafter, two optimisation methods are applied. The first method is carried out manually by varying a single parameter and investigating its effect on torque density. The second method is conducted automatically using OptiNet to validate the results achieved with the manual optimisation. Further analysis is carried out, including mechanical evaluation against centrifugal forces in order to determine the mechanical strength of the rotor. Finally, a prototype is constructed and tested with detailed discussion on the results achieved and comparison with finite element calculations in order to validate the design and determine its level of performance. The final design is then compared to the Toyota Camry and Prius electrical motors.