3 research outputs found
An Online Pricing Mechanism for Electric Vehicle Parking Assignment and Charge Scheduling
In this paper, we design a pricing framework for online electric vehicle (EV)
parking assignment and charge scheduling. Here, users with electric vehicles
want to park and charge at electric-vehicle-supply-equipment (EVSEs) at
different locations and arrive/depart throughout the day. The goal is to assign
and schedule users to the available EVSEs while maximizing user utility and
minimizing operational costs. Our formulation can accommodate multiple
locations, limited resources, operational costs, as well as variable arrival
patterns. With this formulation, the parking facility management can optimize
for behind-the-meter solar integration and reduce costs due to procuring
electricity from the grid. We use an online pricing mechanism to approximate
the EVSE reservation problem's solution and we analyze the performance compared
to the offline solution. Our numerical simulation validates the performance of
the EVSE reservation system in a downtown area with multiple parking locations
equipped with EVSEs.Comment: 6 pages, 2 figures. To Appear, ACC 2019, Philadelphia, US
Pricing and Routing Mechanisms for Differentiated Services in an Electric Vehicle Public Charging Station Network
We consider a Charging Network Operator (CNO) that owns a network of Electric
Vehicle (EV) public charging stations and wishes to offer a menu of
differentiated service options for access to its stations. This involves
designing optimal pricing and routing schemes for the setting where users
cannot directly choose which station they use. Instead, they choose their
priority level and energy request amount from the differentiated service menu,
and then the CNO directly assigns them to a station on their path. This allows
higher priority users to experience lower wait times at stations, and allows
the CNO to directly manage demand, exerting a higher level of control that can
be used to manage the effect of EV on the grid and control station wait times.
We consider the scenarios where the CNO is a social welfare-maximizing or a
profit-maximizing entity, and in both cases, design pricing-routing policies
that ensure users reveal their true parameters to the CNO
Vehicle trajectory prediction in top-view image sequences based on deep learning method
Annually, a large number of injuries and deaths around the world are related
to motor vehicle accidents. This value has recently been reduced to some
extent, via the use of driver-assistance systems. Developing driver-assistance
systems (i.e., automated driving systems) can play a crucial role in reducing
this number. Estimating and predicting surrounding vehicles' movement is
essential for an automated vehicle and advanced safety systems. Moreover,
predicting the trajectory is influenced by numerous factors, such as drivers'
behavior during accidents, history of the vehicle's movement and the
surrounding vehicles, and their position on the traffic scene. The vehicle must
move over a safe path in traffic and react to other drivers' unpredictable
behaviors in the shortest time. Herein, to predict automated vehicles' path, a
model with low computational complexity is proposed, which is trained by images
taken from the road's aerial image. Our method is based on an encoder-decoder
model that utilizes a social tensor to model the effect of the surrounding
vehicles' movement on the target vehicle. The proposed model can predict the
vehicle's future path in any freeway only by viewing the images related to the
history of the target vehicle's movement and its neighbors. Deep learning was
used as a tool for extracting the features of these images. Using the HighD
database, an image dataset of the road's aerial image was created, and the
model's performance was evaluated on this new database. We achieved the RMSE of
1.91 for the next 5 seconds and found that the proposed method had less error
than the best path-prediction methods in previous studies