524 research outputs found
Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-in Hybrid Electric Buses
We discuss a recently introduced ECO-driving concept known as SPONGE in the
context of Plug-in Hybrid Electric Buses (PHEB)'s.Examples are given to
illustrate the benefits of this approach to ECO-driving. Finally, distributed
algorithms to realise SPONGE are discussed, paying attention to the privacy
implications of the underlying optimisation problems.Comment: This paper is recently submitted to the IEEE Transactions on
Automation Science and Engineerin
Simulation Based Studies on the Integration of Battery-Electric Vehicles in Regional Bus Services
Around the world, more and more cities already have introduced electric-powered bus fleets or are planning to do
so in the near future. Thus far, electrical-powered bus fleets have not yet been applied to regional bus services. This paper analyzes the boundary conditions of the operational integration of electrically powered bus fleets in regional bus services. The paper introduces a methodical approach which can be used to identify the optimum deployment strategy for electrical-powered buses which is a combination of technological factors (e.g. dimensions of energy storage and charging technology) the operational
regime to be applied (e.g. routing and scheduling). The methodical approach is exemplarily instantiated with the analysis of sample data of a bus line in a topographically challenging region. The model-driven prognosis of the vehicle’s state of charge is the basis for the definition of an optimum deployment strategy for electrical-powered buses on that line
Topics in Electromobility and Related Applications
In this thesis, we mainly discuss four topics on Electric Vehicles (EVs) in the context of
smart grid and smart transportation systems.
The first topic focuses on investigating the impacts of different EV charging strategies on
the grid. In Chapter 3, we present a mathematical framework for formulating different EV
charging problems and investigate a range of typical EV charging strategies with respect to
different actors in the power system. Using this framework, we compare the performances of
all charging strategies on a common power system simulation testbed, highlighting in each
case positive and negative characteristics.
The second topic is concerned with the applications of EVs with Vehicle-to-Grid (V2G)
capabilities. In Chapter 4, we apply certain ideas from cooperative control techniques to
two V2G applications in different scenarios. In the first scenario, we harness the power
of V2G technologies to reduce current imbalance in a three-phase power network. In the
second scenario, we design a fair V2G programme to optimally determine the power dispatch
from EVs in a microgrid scenario. The effectiveness of the proposed algorithms are verified
through a variety of simulation studies.
The third topic discusses an optimal distributed energy management strategy for power
generation in a microgrid scenario. In Chapter 5, we adapt the synchronised version of the
Additive-Increase-Multiplicative-Decrease (AIMD) algorithms to minimise a cost utility
function related to the power generation costs of distributed resources. We investigate the
AIMD based strategy through simulation studies and we illustrate that the performance of
the proposed method is very close to the full communication centralised case. Finally, we
show that this idea can be easily extended to another application including thermal balancing
requirements.
The last topic focuses on a new design of the Speed Advisory System (SAS) for optimising
both conventional and electric vehicles networks. In Chapter 6, we demonstrate that, by
using simple ideas, one can design an effective SAS for electric vehicles to minimise group
energy consumption in a distributed and privacy-aware manner; Matlab simulation are give
to illustrate the effectiveness of this approach. Further, we extend this idea to conventional
vehicles in Chapter 7 and we show that by using some of the ideas introduced in Chapter
6, group emissions of conventional vehicles can also be minimised under the same SAS
framework. SUMO simulation and Hardware-In-the-Loop (HIL) tests involving real vehicles
are given to illustrate user acceptability and ease of deployment.
Finally, note that many applications in this thesis are based on the theories of a class
of nonlinear iterative feedback systems. For completeness, we present a rigorous proof on
global convergence of consensus of such systems in Chapter 2
Planning UAV Activities for Efficient User Coverage in Disaster Areas
Climate changes brought about by global warming as well as man-made
environmental changes are often the cause of sever natural disasters. ICT,
which is itself responsible for global warming due to its high carbon
footprint, can play a role in alleviating the consequences of such hazards by
providing reliable, resilient means of communication during a disaster crisis.
In this paper, we explore the provision of wireless coverage through UAVs
(Unmanned Aerial Vehicles) to complement, or replace, the traditional
communication infrastructure. The use of UAVs is indeed crucial in emergency
scenarios, as they allow for the quick and easy deployment of micro and pico
cellular base stations where needed. We characterize the movements of UAVs and
define an optimization problem to determine the best UAV coverage that
maximizes the user throughput, while maintaining fairness across the different
parts of the geographical area that has been affected by the disaster. To
evaluate our strategy, we simulate a flooding in San Francisco and the car
traffic resulting from people seeking safety on higher ground
Analyzing the Impact of Roadmap and Vehicle Features on Electric Vehicles Energy Consumption
Electric Vehicles (EVs) market penetration rate is continuously increasing due to several aspects such as pollution reduction initiatives, government incentives, cost reduction, and fuel cost increase, among others. In the vehicular field, researchers frequently use simulators to validate their proposals before implementing them in real world, while reducing costs and time. In this work, we use our ns-3 network simulator enhanced version to demonstrate the influence of the map layout and the vehicle features on the EVs consumption. In particular, we analyze the estimated consumption of EVs simulating two different scenarios: (i) a segment of the E313 highway, located in the north of Antwerp, Belgium and (ii) the downtown of the city of Antwerp with real vehicle models. According to the results obtained, we demonstrate that the mass of the vehicle is a key factor for energy consumption in urban scenarios, while in contrast, the Air Drag Coefficient (C-d) and the Front Surface Area (FSA) play a critical role in highway environments. The most popular and powerful simulations tools do no present combined features for mobility, realistic map-layouts and electric vehicles consumption. As ns-3 is one of the most used open source based simulators in research, we have enhanced it with a realistic energy consumption feature for electric vehicles, while maintaining its original design and structure, as well as its coding style guides. Our approach allows researchers to perform comprehensive studies including EVs mobility, energy consumption, and communications, while adding a negligible overhead
New Perspectives on Modelling and Control for Next Generation Intelligent Transport Systems
This PhD thesis contains 3 major application areas all within an Intelligent Transportation
System context.
The first problem we discuss considers models that make beneficial use of the large
amounts of data generated in the context of traffic systems. We use a Markov chain
model to do this, where important data can be taken into account in an aggregate form.
The Markovian model is simple and allows for fast computation, even on low end computers,
while at the same time allowing meaningful insight into a variety of traffic system
related issues. This allows us to both model and enable the control of aggregate, macroscopic
features of traffic networks. We then discuss three application areas for this model:
the modelling of congestion, emissions, and the dissipation of energy in electric vehicles.
The second problem we discuss is the control of pollution emissions in
eets of hybrid
vehicles. We consider parallel hybrids that have two power units, an internal combustion
engine and an electric motor. We propose a scheme in which we can in
uence the mix
of the two engines in each car based on simple broadcast signals from a central infrastructure.
The infrastructure monitors pollution levels and can thus make the vehicles
react to its changes. This leads to a context aware system that can be used to avoid pollution
peaks, yet does not restrict drivers unnecessarily. In this context we also discuss
technical constraints that have to be taken into account in the design of traffic control
algorithms that are of a microscopic nature, i.e. they affect the operation of individual
vehicles. We also investigate ideas on decentralised trading of emissions. The goal here
is to allocate the rights to pollute fairly among the
eet's vehicles.
Lastly we discuss the usage of decentralised stochastic assignment strategies in traffic
applications. Systems are considered in which reservation schemes can not reliably be
provided or enforced and there is a signifficant delay between decisions and their effect. In
particular, our approach facilitates taking into account the feedback induced into traffic
systems by providing forecasts to large groups of users. This feedback can invalidate the
predictions if not modelled carefully. At the same time our proposed strategies are simple
rules that are easy to follow, easy to accept, and significantly improve the performance
of the systems under study. We apply this approach to three application areas, the assignment
of electric vehicles to charging stations, the assignment of vehicles to parking
facilities, and the assignment of customers to bike sharing stations.
All discussed approaches are analysed using mathematical tools and validated through
extensive simulations
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