Smart Charging Strategy for Electric
Vehicle Charging Stations
Published in: IEEE Transactions on Transportation
Electrification ( Volume: 4, Issue: 1, March 2018)
Date of Publication: 18 September 2017
Authors : Zeinab Moghaddam; Iftekhar Ahmad; Daryoush Habibi; Quoc
Viet Phung
Abstract:
Although the concept of transportation electrification holds enormous
prospects in addressing the global environmental pollution problem, in
reality the market penetration of plug-in electric vehicles (PEVs) has been
very low. Consumer concerns over the limited availability of charging
facilities and unacceptably long charging periods are major factors behind
this low penetration rate. From the perspective of the electricity grid, a
longer PEV peak load period can potentially overlap with the residential
peak load period, making energy management more challenging. A suitably
designed charging strategy can help to address these concerns, which
motivated us to conduct this research. In this paper, we present a smart
charging strategy for a PEV network that offers multiple charging options,
including ac level 2 charging, dc fast charging, and battery swapping
facilities at charging stations. For a PEV requiring charging facilities, we
model the issue of finding the optimal charging station as a multiobjective
optimization problem, where the goal is to find a station that ensures the
minimum charging time, travel time, and charging cost. We extend the
model to a metaheuristic solution in the form of an ant colony optimization.
Simulation results show that the proposed solution significantly reduces
waiting time and charging cost.
Real-Time Forecasting of EV Charging
Station Scheduling for Smart Energy
Systems
Submission received: 7 November 2016
Revised: 11 March 2017
Accepted: 13 March 2017
Published: 16 March 2017
Authors : Bharatiraja Chokkalingam , Sanjeevikumar Padmanaban , Pierluigi
Siano , Ramesh Krishnamoorthy , Raghu Selvaraj
Abstract:
The enormous growth in the penetration of electric vehicles (EVs), has laid the
path to advancements in the charging infrastructure. Connectivity between
charging stations is an essential prerequisite for future EV adoption to alleviate
user’s “range anxiety”. The existing charging stations fail to adopt power
provision, allocation and scheduling management. To improve the existing
charging infrastructure, data based on real-time information and availability of
reserves at charging stations could be uploaded to the users to help them
locate the nearest charging station for an EV. This research article focuses on an
a interactive user application developed through SQL and PHP platform to
allocate the charging slots based on estimated battery parameters, which uses
data communication with charging stations to receive the slot availability
information. The proposed server-based real-time forecast charging
infrastructure avoids waiting times and its scheduling management efficiently
prevents the EV from halting on the road due to battery drain out. The
proposed model is implemented using a low-cost microcontroller and the
system etiquette tested.
A Smart Coordination System Integrates MCS
to Minimize EV Trip Duration and Manage
the EV Charging, Mainly at Peak Times
Received: 4 September 2020
Revised: 19 April 2021
Accepted: 26 April 2021
Authors : Ibrahim El-fedany · Driss Kiouach · Rachid Alaoui
Abstract:
The fxed public charging stations (FCS) network is challenged by widespread of
electric vehicle (EV) uses. Therefore, there is exploitation of the many parks
spread over the territory of a smart city by means of mobile charging stations
(MCS). That can be set up or moved anywhere as needed. This allows for the
rapid expansion of the charging infrastructure. In this work, we propose an
architecture system consisting of a set of algorithms to manage electric vehicle
charging plans in terms of minimizing journey time, including waiting and
charging time at charging stations (CS). Thus, During the CS selection decision,
the system takes into consideration the amount of sufcient energy for the EV to
reach the specifed CS, the remaining amount of energy in stock if the selected
CS is the MCS type, the CS Real-time status, and the frst-come-frst-served
policy based on providing charge seats in CS. Moreover, the dynamically system
regulates each FCS at its peak time of its MCS operation, ensuring a semi-
permanent equilibrium in electrical grid usage and reducing congestion by
changing the fow of vehicles that are directed towards FCSs for charging. The
evaluation results demonstrate, in the context of the Helsinki City scenario, the
efectiveness of the proposed system and algorithms, in terms of achieving the
above-mentioned objectives.
