TY - JOUR
T1 - Adaptive Energy Management System for Electric Vehicle Charging Stations
T2 - 2024 International Conference on Renewable Energy Resources and Applications, ICRERA 2024
AU - Bostani, Ali
AU - Kulshreshtha, Kushagra
AU - Agarkar, A. A.
AU - Karthika, K.
AU - Sarathy, K.
AU - Pawar, Avinash M.
AU - Ashreetha, B.
N1 - Publisher Copyright:
© The Authors, published by EDP Sciences.
PY - 2024/11/14
Y1 - 2024/11/14
N2 - The increasing demand for electric vehicles (EVs) presents significant challenges for energy grids, particularly in balancing demand and supply during peak charging periods. This paper proposes an Adaptive Energy Management System (EMS) for EV charging stations that leverages artificial intelligence (AI) techniques to optimize power distribution and enhance grid stability. By integrating fuzzy logic and reinforcement learning algorithms, the proposed system dynamically adjusts charging power allocation based on real-time grid conditions and EV battery levels. The EMS ensures efficient energy use, minimizes grid overload risks, and enables seamless integration with renewable energy sources. Simulation results demonstrate the system’s ability to maintain grid stability while maximizing charging efficiency. This adaptive approach paves the way for future smart grid applications, offering scalability and robustness for large-scale EV deployments.
AB - The increasing demand for electric vehicles (EVs) presents significant challenges for energy grids, particularly in balancing demand and supply during peak charging periods. This paper proposes an Adaptive Energy Management System (EMS) for EV charging stations that leverages artificial intelligence (AI) techniques to optimize power distribution and enhance grid stability. By integrating fuzzy logic and reinforcement learning algorithms, the proposed system dynamically adjusts charging power allocation based on real-time grid conditions and EV battery levels. The EMS ensures efficient energy use, minimizes grid overload risks, and enables seamless integration with renewable energy sources. Simulation results demonstrate the system’s ability to maintain grid stability while maximizing charging efficiency. This adaptive approach paves the way for future smart grid applications, offering scalability and robustness for large-scale EV deployments.
KW - Artificial Intelligence
KW - Electric Vehicle Charging
KW - Energy Management System
KW - Fuzzy Logic
KW - Grid Stability
UR - http://www.scopus.com/inward/record.url?scp=85211818570&partnerID=8YFLogxK
U2 - 10.1051/e3sconf/202459104002
DO - 10.1051/e3sconf/202459104002
M3 - Conference article
AN - SCOPUS:85211818570
SN - 2555-0403
VL - 591
JO - E3S Web of Conferences
JF - E3S Web of Conferences
M1 - 04002
Y2 - 26 September 2024 through 27 September 2024
ER -
