69,721 research outputs found
Developing and Evaluating the driving and powertrain systems of automated and electrified vehicles (AEVs) for sustainable transport
In the transition towards sustainable transport, automated and electrified vehicles (AEVs) play a key role in overcoming challenges such as fuel consumption, emissions, safety, and congestion. The development and assessment of AEVs require bringing together insights from multiple disciplines such as vehicle studies to design and control AEVs and traffic flow studies to describe and evaluate their driving behaviours. This thesis, therefore, addresses the needs of automotive and civil engineers, and investigates three classes of problems: optimizing the driving and powertrain systems of AEVs, modelling their driving behaviours in microscopic traffic simulation, and evaluating their performance in real-world driving conditions.
The first part of this thesis proposes Pareto-based multi-objective optimization (MOO) frameworks for the optimal sizing of powertrain components, e.g., battery and ultracapacitor, and for the integrated calibration of control systems including adaptive cruise control (ACC) and energy management strategy (EMS). We demonstrate that these frameworks can bring collective improvements in energy efficiency, greenhouse gas (GHG) emissions, ride comfort, safety, and cost-effectiveness.
The second part of this thesis develops microscopic free-flow or car-following models for reproducing longitudinal driving behaviours of AEVs in traffic simulation, which can support the needs to predict the impact of AEVs on traffic flow and maximize their benefits to the road network. The proposed models can account for electrified vehicle dynamics, road geometric characteristics, and sensing/perception delay, which have significant effects on driving behaviours of AEVs but are largely ignored in traffic flow studies.
Finally, we systematically evaluate the energy and safety performances of AEVs in real-world driving conditions. A series of vehicle platoon experiments are carried out on public roads and test tracks, to identify the difference in driving behaviours between ACC-equipped vehicles and human-driven vehicles (HDVs) and to examine the impact of ACC time-gap settings on energy consumption
Modelling supported driving as an optimal control cycle: Framework and model characteristics
Driver assistance systems support drivers in operating vehicles in a safe,
comfortable and efficient way, and thus may induce changes in traffic flow
characteristics. This paper puts forward a receding horizon control framework
to model driver assistance and cooperative systems. The accelerations of
automated vehicles are controlled to optimise a cost function, assuming other
vehicles driving at stationary conditions over a prediction horizon. The
flexibility of the framework is demonstrated with controller design of Adaptive
Cruise Control (ACC) and Cooperative ACC (C-ACC) systems. The proposed ACC and
C-ACC model characteristics are investigated analytically, with focus on
equilibrium solutions and stability properties. The proposed ACC model produces
plausible human car-following behaviour and is unconditionally locally stable.
By careful tuning of parameters, the ACC model generates similar stability
characteristics as human driver models. The proposed C-ACC model results in
convective downstream and absolute string instability, but not convective
upstream string instability observed in human-driven traffic and in the ACC
model. The control framework and analytical results provide insights into the
influences of ACC and C-ACC systems on traffic flow operations.Comment: Submitted to Transportation Research Part C: Emerging Technologie
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
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