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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

Applicability of federal motor vehicle standards to electric and hybrid vehicles.

Mode of access: Internet.

Electric vehicles and displaced gaseous emission

Electric vehicles (EV) do not emit tailpipe exhaust fumes in the same manner as internal combustion engine vehicles. Optimal benefits can only be achieved, if EVS are deployed effectively, so that the tailpipe emissions are not substituted by additional emissions in the electricity sector. This paper examines the potential contributions that Plug in Hybrid Electric Vehicles can make in reducing carbon dioxide. The paper presents the results of the generation expansion model for Northern Ireland and the Republic of Ireland built using the dynamic programming based long term generation expansion planning tool called the Wien Automatic System Planning IV tool. The model optimizes power dispatch using hourly electricity demand curves for each year 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. In order to simulate the effect of PHEV, two distinct charging scenarios are applied based on a peak tariff and an off peak tariff. The importance and influence of the charging regime on the amount of energy used and gaseous emissions displaced is determined and discussed.

Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.

Optimal Supervisory Control of Hybrid Vehicles

Hybrid vehicles (HV), comprising a conventional ICE-based powertrain and a secondary energy source, to be converted into mechanical power as well, represent a well-established alternative to substantially reduce both fuel consumption and tailpipe emissions of passenger cars. Several HV architectures are either being studied or already available on market, e.g. Mechanical, Electric, Hydraulic and Pneumatic Hybrid Vehicles. Among the others, Electric (HEV) and Mechanical (HSF-HV) parallel Hybrid configurations are examined throughout this Thesis. To fully exploit the HVs potential, an optimal choice of the hybrid components to be installed must be properly designed, while an effective Supervisory Control must be adopted to coordinate the way the different power sources are managed and how they interact. Real-time controllers can be derived starting from the obtained optimal benchmark results. However, the application of these powerful instruments require a simplified and yet reliable and accurate model of the hybrid vehicle system. This can be a complex task, especially when the complexity of the system grows, i.e. a HSF-HV system assessed in this Thesis. The first task of the following dissertation is to establish the optimal modeling approach for an innovative and promising mechanical hybrid vehicle architecture. It will be shown how the chosen modeling paradigm can affect the goodness and the amount of computational effort of the solution, using an optimization technique based on Dynamic Programming. The second goal concerns the control of pollutant emissions in a parallel Diesel-HEV. The emissions level obtained under real world driving conditions is substantially higher than the usual result obtained in a homologation cycle. For this reason, an on-line control strategy capable of guaranteeing the respect of the desired emissions level, while minimizing fuel consumption and avoiding excessive battery depletion is the target of the corresponding section of the Thesis.

Bayesian hybrid algorithms and models : implementation and associated issues

This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.

An intelligent network control system for plug-in electric vehicles and the associate communication mechanism

The development of an intelligent plug-in electric vehicle (PEV) network is an important research topic in the smart grid environment. An intelligent PEV network enables a flexible control of PEV charging and discharging activities and hence PEVs can be utilized as ancillary service providers in the power system concerned. Given this background, an intelligent PEV network architecture is first developed, and followed by detailed designs of its application layers, including the charging and discharging controlling system, mobility and roaming management, as well as communication mechanisms associated. The presented architecture leverages the philosophy in mobile communication network buildup