Vehicle type approval CO2 emission figures form the basis for many countries’ national policy to reduce transport's contribution to anthropogenic climate change. However, it has become increasingly apparent that the vehicle type approval testing procedure used in Europe is not fit for purpose. There is, therefore, a need for representative real world emission factors that can be used to inform consumers, aid policy makers and provide an accurate benchmark from which type approval figures can be compared. In this work, two methods are explored to assess their feasibility to provide robust CO2 emission figures. The first is on-road vehicle activity tracking, using data collected from the vehicle controller area network. This method was chosen as it has the potential to provide large quantities of cheap, reliable data and has been demonstrated by recording over 40 parameters during testing of a third-generation Toyota Prius. This data has been used to analyse the vehicle powertrain control and provide a clear understanding of the control mechanisms that balance the engine and electrical power systems, present a comparison of the emissions of conventional and hybrid taxis giving local policy makers the underlying evidence required to introduce strong policies to reduce urban emissions from taxis and build a microscale emission model for accurate and detailed emission forecasts. The second method is microscale vehicle modelling, defined as very short time step models (1 second or less) that capture vehicle and location specific details within the model. The model requires vehicle speed and road gradient data as input and outputs second-by-second cumulative and total fuel consumed and CO2 emissions. The model has been validated against independent data (chassis dynamometer data collected by Argonne National Laboratory) and is now a powerful tool to help assess the effects of local policies (geofences, changes in the speed limit, incentives for hybrid vehicle uptake) or schemes (eco-driving) on the CO2 emissions from hybrid vehicles. This work has further developed these two methods in two ways. Firstly, by demonstrating the accuracy of controller area network data collected in vehicle activity tracking. Secondly, by demonstrating the precision of emission models built using real-world data, despite the data noise caused by real world conditions. In conclusion, these methods are well suited to providing representative real world CO2 emission factors, especially if the methods are combined. This is because vehicle activity tracking can provide the large amount of data needed for vehicle modelling and a vehicle model can provide situation specific emission factors, which, in contrary to many current emissions factors, are not only dependent on vehicle average speeds.

Profit margins in logistics are very tight and reducing fuel costs is critical to remain competitive. Customers and policy makers are becoming more sensitive towards climate change due to the links between fossil fuels and global warming. This research presents a framework to help decision makers to select the optimal heavy goods vehicles’ specification that minimises their carbon emissions cost-efficiently given their aversion to risk. The framework developed, uses a broad range of methodologies, techniques and tools including carbon emission lifecycle analysis; simulations; live trials; statistical analysis; metaheuristics and multicriteria decision analysis. An assessment of the waste-to-fuel opportunities of quick service restaurants showed that these could cover around 5% of the energy needs of UK commercial fleets and it was found that used cooking oil could reduce diesel emissions by over 85%. Among the range of scenarios built, the solution recommended by the framework indicated that all vehicles should fit spray reduction mudflaps, low rolling resistance tyres, automatic tyre monitoring systems and lightweight materials. While urban HGVs should also have automatic manual transmissions, regional and long-haul HGVs should include aerodynamic trailers and predictive cruise control instead. Compared to the do-nothing scenarios, the net present costs of urban, regional and long-haul vehicles can be reduced by 3%, 9.4% and 10.7% and their GHG emissions by 7%, 14.6% and 17.1%, respectively. This results in savings between £2.7M to £4.4M and 7,950 to 8,262 t CO2 eq. for the whole fleet of the industrial sponsor over 5 years. The lowest cost solution could save £5.8M and 27,684 t CO2 while carbon minimisation one could save over 30,000 tonnes and £2.9M, with current energy prices. The results suggest that diesel technology HGVs can still play a role in the decarbonisation of road haulage and that the uptake of low carbon technologies is highly influence by the risks aversion of the decision maker and duty cycle. The results demonstrate that the EU 2020 targets of delivering 10% savings from road transport by 2020 are perfectly feasible.

Modern military vehicles are increasingly complex systems that rely on vehicle electronics (vetronics) to provide essential capabilities. The electronic architecture of a military vehicle consists of distributed subsystems of varying degree of integrity that are integrated using the vetronics infrastructure. Many of these subsystems are rapidly updated to address Urgent Operational Requirements (UORs). The integration of these subsystems often provides capabilities greater than the sum of the individual subsystems. Therefore, the complex and integrated vetronics architecture is a critical element of these vehicles. However, the safety and reliability certification of the distributed and integrated capabilities is becoming increasingly difficult. The traditional approach to vehicle design and development lacks in managing the increased complexity of the integrated architecture and the complexity of the safety justification.

While the fuel resource scarcity and the environment crisis are becoming two of the major problems for the human society in the new century, in the context that the on-road transport is the largest energy-consuming society sector, electrical vehicles (EVs) serving resource-sustainable environmentally-clean transport attract increasing attention. Permanent magnet (PM) traction machines with high torque and power densities, and high energy efficiency gain great interest from the engineer and industry communities. Jointly with the chances, fresh challenges are brought by EVs to PM machine designers. In response to the vehicles' call for high speed, mighty acceleration, long range, safe and robust system, the engineers need to develop a powerful design platform that allows for multi-physic evaluations of machine designs over a large torque-speed range, especially for energy efficiency, PM health, and thermal-withstanding ability. More importantly, these evaluations must be against driving cycles rather than on a single rated operation point in catering to various real-world driving conditions. The complex and enormous computation efforts required necessitate new, effective and feasible design techniques. In this work a set of modelling techniques for PM machines are developed, in order to establish a computationally-efficient yet accurate design and optimisation method for EV traction PM machines. Through the method, comprehensive machine multi-physics assessments against driving cycles are enabled; electro-thermally coupled evaluation is achieved; 3-dimensional eddy-current loss of PMs are accurately monitored in the context of PM protection; lastly with the techniques integrated together, a fast and effective optimisation method for EV traction PM machines is acquired. To exploit the benefits of the proposed method, a design, optimisation and manufacture process of PM machines for a light-duty EV distributed traction system is formatted, which includes a quantity assessment of machine topologies, investigation of driving cycle influence on designs, optimisation of the selected 18-slot 8-pole interior PM machine against a series of EV machine design criteria, the subsequent prototype experiments, and optimisation of combinations and power split ratios of PM machines for the distributed traction system. Through the design process for the EV traction system and experiments, the effectiveness, computational efficiency, and accuracy of the proposed designing methods are exhibited and validated.

The combustion of fossil fuels, a non-renewable and finite resource, has caused increasing air pollution, ozone damage, acid rain and global warming. Electric vehicles (EVs) are eco-friendly with the attractive properties of lower greenhouse emissions, lower fuel usage and reduced air pollution. Battery and charger is one of the vital elements to analyse and develop for EVs. This research focuses on the bidirectional on-board battery charger with the emphasis on the design and analysis of the DC-DC converter. Firstly, a LLC resonant topology is selected as the initial design candidate of the DC-DC resonant converter owing to the preferred soft-switching features. Single phase chargers suffer from a second harmonic voltage/current ripple which can lead to reduction of battery life. A feedforward-proportional-integral-resonant (FF-PIR) controller has been proposed and tested for suppressing this low-frequency current ripple in the LLC resonant converter employed in EV battery chargers. Secondly, the recent developments in smart-grid technology necessitates bi-directional power flow from distributed energy storages like EV to support grid in the vehicle-to-grid (V2G) application. To achieve bi-directional power flow capability, the conventional uni-directional LLC topology is modified into bidirectional CLLC resonant converter. The characteristics have been analysed and validated by extensive simulations and experimental tests. Further improvement has been implemented to increase the power efficiency under varying battery voltages. An optimum-resonant-frequency tracking scheme is proposed and tested to maintain the operation close to the maximum efficiency point over a wide battery voltage range. Finally, in order to predict the converter efficiency accurately, this thesis presents a prediction method employing 2D and 3D finite element analysis (FEA) for calculating the power losses of magnetic components with litz wire in the converter.

This research work proposes a fast computing tool for performance evaluation of electrical machines. It focuses on Interior Permanent Magnet (IPM) machines, where a combination of Finite-Element (FE) method and mathematical approximations are used to predict iron losses and eddy current losses in the magnets in addition to torque-speed characteristics. These losses are then used to generate the efficiency map of the machine following a set criterion. The adopted tool is based on FE and computes the complete machines' characteristic semi-analytically without any further FE computation. For the initial analysis and model verification a 16-pole 24-slot Interior Permanent Magnet (IPM) is adopted. The results obtained, compared to completely FE-based solution show that the proposed tool predicts the electromagnetic losses effectively within less computation time. Finally, Experimental work done on an existing IPM traction machine is described and the obtained results are confronted to the predicted ones.

Electric vehicles have been identified as a key technology to decarbonise road transport which accounts for approximately a quarter of the UK’s greenhouse gas end-user emissions (DECC, 2014). The diffusion of electric vehicles requires a transformation across a large spectrum of societal and economic dimensions. Using research and data over the study period from 2011 to late 2015, this research examines the pathways for transition from today’s personal transport mix to one focused on electric vehicles, the research identifies path dependencies and lock-in of the internal combustion engine that results in sustained usage patterns. The relationship between transition pathways, path dependencies, technology lock-in and e-mobility has been under-researched. Commercial urban fleets and car clubs were identified as key market opportunities for electric vehicles. Total Cost of Ownership studies and in-depth interviews with industry experts were conducted to identify existing transition barriers for the adoption of electric vehicles within these applications. Established path dependencies of the internal combustion engine and consequent technology lock-in were found to stem from an inter-dependant and reinforcing technology system of roads, service stations, parking facilities and societal status. In order to achieve integrated transport services –and EVs as part of it - alternative business models are to redefine humankind’s relationship with the car through systemic innovation and competitive finance models. The multi-level perspective was used to determine the dynamics of change for fleets towards electric vehicles, and the roles of different stakeholder types were explored through the ‘action space’ of government, civil society, market and governance logics. The results indicate that the diffusion of niche technologies and business models are establishing individual pathways within the two markets. It is evident within the fleet and car club markets that an ‘innovator logic’ is competing within the action space to unlock EVs through technology innovation that extends beyond the transitional role of hybrids. However, fundamental to each market is the parallel role of government to invest in R&D and motivation crowding to remove lock-in and destabilise the existing regime.

A novel new application of optimisation algorithm “Bess Algorithm” in the design of electric machine is presented in this thesis. The optimisation has the ability to perform global and local search and can be applied on constrained, unconstrained optimisation problem with multi-objective function, which all counted when consider optimisation algorithm for the design of electric machine. The searching procedure of the optimisation algorithm has been described in detailed. Furthermore, novel instructions and recommendation were implemented to tune the optimisation parameters, particularly for the purpose electric machine design, which in turn reduced the search space, increase efficiency and ability to find optimal solution with lower computation time. The optimisation was applied to search for optimal parameters of a benchmark electric machine with multi-objective to reduce the cost and increase the power density, power-volume ratio and efficiency. Throughout the thesis, a full detailed analytical model for the design of brushless permanent magnet synchronous motor that account for electromagnetic and thermal aspects was described. The optimisation was employed to search for optimal parameters of the analytical model that satisfy the design requirements. Then, the generated optimal parameters were evaluated and verified by Finite Element Analysis, FEA. The results from the FEA show good agreement with their corresponding values in the analytical model within acceptable range. At the same operational conditions and output specifications, the results show that the power density, volume to power ratio and cost of the new optimised motor IV were all increased by 19%, 39%, 24% respectively and the efficiency reduced only by -1%. The optimisation was also compared with one of the most usable optimisation algorithm used in the design of electric machine i.e. Genetic Algorithm. The results show that bees algorithm has more ability to cover the search space with less number of recruited bees and less number of iterations and higher computation efficiency.

In the present day fossil fuel availability, cost, security and the pollutant emissions resulting from its use have driven industry into looking for alternative ways of powering vehicles. The aim of this research is to synthesize/design and develop a framework of novel control architectures which can result in complex powered vehicle subsystems to perform better with reduced exogeneuous information. This research looks into the area of energy management by proposing an intelligent based system which not only looks at the beaten path of where energy comes from and how much of it to use, but it goes further by taking into consideration the world around it. By operating without GPS, it realies on data such as usage, average consumption, system loads and even other surrounding vehicles are considered when making the difficult decisions of where to direct the energy into, how much of it, and even when to cut systems off in benefit of others. All this is achieved in an integrated way by working within the limitations of non-fossil fuelled energy sources like fuel cells, ultracapacitors and battery banks using driver-provided information or by crafting an artificial usage profile from historicaly learnt data. By using an organic computing philosophy based on artificial intelligence this alternative approach to energy supply systems presents a different perspective beginning by accepting the fact that when hardware is set energy can be optimized only so much and takes a step further by answering the question of how to best manage it when refuelling might not be an option. The result is a situationally aware system concept that is portable to any type of electrically powered platform be it ground, aerial or marine since it operates on the fact that all operate within three dimensional space. The system´s capabilities are then verified in a virtual reality environment which can be tailored to the meet reseach needs including allowing for different altitudes, environmental temperature and humidity profiles. This VR system is coupled with a chassis dynamometer to allow for testing of real physical prototype unmanned ground vehicles where the intelligent system will benefit by learning from real platform data. The Thesis contributions and objectives are summarised next:  The control system proposed includes an awareness of the surroundings within which the vehicle is operating without relying on GPS position information.  The system proposed is portable and could be used to control other systems.  The test platform developed within the Thesis is flexible and could be used for other systems.  The control system for the fuel cell system described within the work has included an allowance for altitude and humidity. These factors would appear to be significant for such systems.  The structure of the control system and its hierarchy is novel.  The ability of the system to be applied to a UAV and as such control a ‘vehicle’ in 3 dimensions, and yet be also applied to a ground vehicle, where roll and pitch are largely a function of the ground over which it travels (so the UGV only uses a subset of the control functionality).  The mission awareness of the control structure appears to be the heart of the potential contribution to knowledge, and that this also includes the ability to create an estimated, artificial mission profile should one not be input by the operators. This learnt / adaptive input could be expanded on to highlight this aspect.

This thesis deals with the energy management problem of series hybrid electric vehicles (HEVs), where the objective is to maximize fuel economy for general driving. The work employs a high-fidelity model that has been refined to deliver appropriate level of dynamics (for the purposes of this research) at an acceptable computational burden. The model is then used to design, test and study established conventional control strategies, which then act as benchmarks and inspiration for proposed novel control strategies. A family of efficiency maximizing map strategies (EMMS) are developed based on a thorough and holistic analysis of the powertrain efficiencies. The real-time variants are found to deliver impressive fuel economy, and the global variant is found to outperform the conventional global benchmark. Two heuristic strategies are developed (exclusive operation strategy (XOS) and optimal primary source strategy (OPSS)) that are found to deliver significantly better fuel economy results, compared to conventional alternatives, and further desirable traits. This is found to be particularly related to the better use of modern start stop systems (SSSs) that has not been considered sufficiently in the past. A global heuristic strategy (GHS) is presented that successfully outperforms the conventional global benchmark without any particularly complex analysis. This exposes some of the limitations of optimization-based techniques that have been developed for simple vehicle models. Lastly, the sensitivity of the performance of the control strategies has been studied for variations in tuning accuracy, SSS efficiency, vehicle initial conditions, and general driving conditions. This allows a deeper insight into each control strategy, exposing strengths and limitations that have not been apparent from past work.