TY - JOUR
T1 - Enhancing the Efficiency of Electric Vehicles Charging Stations Based on Novel Fuzzy Integer Linear Programming
AU - Hussain, Shahid
AU - Irshad, Reyazur Rashid
AU - Pallonetto, Fabiano
AU - Jan, Qasim
AU - Shukla, Saurabh
AU - Thakur, Subhasis
AU - Breslin, John G.
AU - Marzband, Mousa
AU - Kim, Yun Su
AU - Rathore, Muhammad Ahmad
AU - El-Sayed, Hesham
N1 - Funding Information:
This project has received funding from the European Union's Horizon Europe research and innovation programme under the project FLOW grant agreement N.101056730. The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Collaboration Funding program grant code (NU/RC/SERC/11/6). This work was also supported in part by a grant from Science Foundation Ireland under Grant Number SFI/16/RC/3918 (Confirm) and Grant Number SFI 12/RC/2289\P2 (Insight), by DTE Network+ funded by EPSRC grant reference EP/S032053/1, and GIST Research Institute(GRI) grant funded by the GIST in 2023.
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - The electric vehicles (EVs) charging stations (CSs) at public premises have higher installation and power consumption costs. The potential benefits of public CSs rely on their efficient utilization. However, the conventional charging methods obligate a long waiting time and thereby deteriorate their efficiency with low utilization. This paper suggests a novel fuzzy integer linear programming and a heuristic fuzzy inference approach (FIA) for CSs utilization. The model introduces the underlying fuzzy inference system and a detailed formulation for obtaining the optimal solution. The developed fuzzy inference incorporates the uncertain and independent available power, required state-of-charge, and dwell time from the power grid and EVs domains and correlates them into weighted control variables. The FIA automates the service provision for the EVs with the most urgent requirements by resolving the objective function utilizing the weighted control variables, thereby optimizing the waiting time and the CSs utilization. To evaluate the effectiveness of the proposed FIA, several case studies were conducted, corresponding to different parking capacities and installations of CSs. Moreover, the simulations were conducted on EVs with varying battery capacities, and their performance was evaluated based on several metrics, including average waiting time, utilization of CSs, fairness, and execution time. The simulation results have confirmed that the effectiveness of the proposed FIA scheduling method is considerably higher than that of the other methods discussed.
AB - The electric vehicles (EVs) charging stations (CSs) at public premises have higher installation and power consumption costs. The potential benefits of public CSs rely on their efficient utilization. However, the conventional charging methods obligate a long waiting time and thereby deteriorate their efficiency with low utilization. This paper suggests a novel fuzzy integer linear programming and a heuristic fuzzy inference approach (FIA) for CSs utilization. The model introduces the underlying fuzzy inference system and a detailed formulation for obtaining the optimal solution. The developed fuzzy inference incorporates the uncertain and independent available power, required state-of-charge, and dwell time from the power grid and EVs domains and correlates them into weighted control variables. The FIA automates the service provision for the EVs with the most urgent requirements by resolving the objective function utilizing the weighted control variables, thereby optimizing the waiting time and the CSs utilization. To evaluate the effectiveness of the proposed FIA, several case studies were conducted, corresponding to different parking capacities and installations of CSs. Moreover, the simulations were conducted on EVs with varying battery capacities, and their performance was evaluated based on several metrics, including average waiting time, utilization of CSs, fairness, and execution time. The simulation results have confirmed that the effectiveness of the proposed FIA scheduling method is considerably higher than that of the other methods discussed.
KW - Charging stations
KW - fuzzy inference system
KW - fuzzy integer linear programming
KW - utilization
KW - waiting time
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U2 - 10.1109/TITS.2023.3274608
DO - 10.1109/TITS.2023.3274608
M3 - Article
AN - SCOPUS:85160263463
SN - 1524-9050
VL - 24
SP - 9150
EP - 9164
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 9
ER -