2,528 research outputs found
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
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Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California
This interdisciplinary eXtensible Building Operating SystemâVehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits
An intelligent multi-speed advisory system using improved whale optimisation algorithm
An intelligent speed advisory system can be used to recommend speed for vehicles travelling in a given road network in cities. In this paper, we extend our previous work where a distributed speed advisory system has been devised to recommend an optimal consensus speed for a fleet of Internal Combustion Engine Vehicles (ICEVs) in a highway scenario. In particular,
we propose a novel optimisation framework where the exact format of each vehicleâs cost function can be implicit, and our algorithm can be used to recommend multiple consensus speeds for vehicles travelling on different lanes in an urban highway scenario. Our studies show that the proposed scheme based on an improved whale optimisation algorithm can effectively reduce CO2 emission generated from ICEVs while providing different
recommended speed options for groups of vehicles
Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems
The Intelligent Transportation System (ITS) is an important part of modern
transportation infrastructure, employing a combination of communication
technology, information processing and control systems to manage transportation
networks. This integration of various components such as roads, vehicles, and
communication systems, is expected to improve efficiency and safety by
providing better information, services, and coordination of transportation
modes. In recent years, graph-based machine learning has become an increasingly
important research focus in the field of ITS aiming at the development of
complex, data-driven solutions to address various ITS-related challenges. This
chapter presents background information on the key technical challenges for ITS
design, along with a review of research methods ranging from classic
statistical approaches to modern machine learning and deep learning-based
approaches. Specifically, we provide an in-depth review of graph-based machine
learning methods, including basic concepts of graphs, graph data
representation, graph neural network architectures and their relation to ITS
applications. Additionally, two case studies of graph-based ITS applications
proposed in our recent work are presented in detail to demonstrate the
potential of graph-based machine learning in the ITS domain
Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility
The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehiclesâ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicleâs technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehiclesâ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio
Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects
© 2013 IEEE. Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of 'smart transport,' Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned
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