81,602 research outputs found
Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data
By using the onboard sensing and external connectivity technology, connected
and automated vehicles (CAV) could lead to improved energy efficiency, better
routing, and lower traffic congestion. With the rapid development of the
technology and adaptation of CAV, it is more critical to develop the universal
evaluation method and the testing standard which could evaluate the impacts on
energy consumption and environmental pollution of CAV fairly, especially under
the various traffic conditions. In this paper, we proposed a new method and
framework to evaluate the energy efficiency and emission of the vehicle based
on the unsupervised learning methods. Both the real-world driving data of the
evaluated vehicle and the large naturalistic driving dataset are used to
perform the driving primitive analysis and coupling. Then the linear weighted
estimation method could be used to calculate the testing result of the
evaluated vehicle. The results show that this method can successfully identify
the typical driving primitives. The couples of the driving primitives from the
evaluated vehicle and the typical driving primitives from the large real-world
driving dataset coincide with each other very well. This new method could
enhance the standard development of the energy efficiency and emission testing
of CAV and other off-cycle credits
Energy harvesting from vehicular traffic over speed bumps: A review
Energy used by vehicles to slow down in areas of limited speed is wasted. A traffic energy-harvesting device (TEHD) is capable of harvesting vehicle energy when passing over a speed bump. This paper presents a classification of the different technologies used in the existing TEHDs. Moreover, an estimation of the energy that could be harvested with the different technologies and their cost has been elaborated. The energy recovered with these devices could be used for marking and lighting of roads in urban areas, making transportation infrastructures more sustainable and environmentally friendly
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
Performance Enhancement of the Flexible Transonic Truss-Braced Wing Aircraft Using Variable-Camber Continuous Trailing-Edge Flaps
Aircraft designers are to a growing extent using vehicle flexibility to optimize performance with objectives such as gust load alleviation and drag minimization. More complex aerodynamically optimized configurations may also require dynamic loads and perhaps eventually flutter suppression. This paper considers an aerodynamically optimized truss-braced wing aircraft designed for a Mach 0.745 cruise. The variable camber continuous trailing edge flap concept with a feedback control system is used to enhance aeroelastic stability. A linearized reduced order aerodynamic model is developed from unsteady Reynolds averaged Navier-Stokes simulations. A static output feedback controller is developed from that model. Closed-loop simulations using the reduced order aerodynamic model show that the controller is effective in stabilizing the vehicle dynamics
New dispatching paradigm in power systems including EV charging stations and dispersed generation: A real test case
Electric Vehicles (EVs) are becoming one of the main answers to the decarbonization of the transport sector and Renewable Energy Sources (RES) to the decarbonization of the electricity production sector. Nevertheless, their impact on the electric grids cannot be neglected. New paradigms for the management of the grids where they are connected, which are typically distribution grids in Medium Voltage (MV) and Low Voltage (LV), are necessary. A reform of dispatching rules, including the management of distribution grids and the resources there connected, is in progress in Europe. In this paper, a new paradigm linked to the design of reform is proposed and then tested, in reference to a real distribution grid, operated by the main Italian Distribution System Operator (DSO), e-distribuzione. First, in reference to suitable future scenarios of spread of RES-based power plants and EVs charging stations (EVCS), using Power Flow (PF) models, a check of the operation of the distribution grid, in reference to the usual rules of management, is made. Second, a new dispatching model, involving DSO and the resources connected to its grids, is tested, using an Optimal Power Flow (OPF) algorithm. Results show that the new paradigm of dispatching can effectively be useful for preventing some operation problems of the distribution grids
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