TY - JOUR
T1 - Optimal energy management of hybrid battery/supercapacitor storage system for electric vehicle application
AU - Rezk, Hegazy
AU - Al-Dhaifallah, Mujahed
AU - Amrr, Syed Muhammad
AU - Alharbi, Abdullah
N1 - Publisher Copyright:
© 2024 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2024/12
Y1 - 2024/12
N2 - A proposed approach for efficient energy management for lithium-ion battery and supercapacitor hybrid energy storage system is outlined in this study. The primary aim is to ensure the electric vehicle receives a stable and high-quality supply of electricity. The suggested management strategy focuses on maintaining the bus voltage at a consistent level while meeting the varying load demands with high-quality power across different scenarios. To achieve this, a management controller is employed, which utilizes a metaheuristics technique to define the parameters of the integral sliding mode control. The supercapacitor units are responsible for controlling the direct current (DC) bus, while the lithium-ion battery plays a role in balancing power distribution on the common line. This study evaluates the potential benefits of integrating salp swarm algorithm techniques into the controller's performance. The findings of the study suggest combining metaheuristic optimization methods with integral sliding mode control. Can lead to improvements in power quality. The proposed management algorithm not only efficiently allocates power resources but also safeguards them, ultimately ensuring a consistent and high-quality power supply for the electric vehicle.
AB - A proposed approach for efficient energy management for lithium-ion battery and supercapacitor hybrid energy storage system is outlined in this study. The primary aim is to ensure the electric vehicle receives a stable and high-quality supply of electricity. The suggested management strategy focuses on maintaining the bus voltage at a consistent level while meeting the varying load demands with high-quality power across different scenarios. To achieve this, a management controller is employed, which utilizes a metaheuristics technique to define the parameters of the integral sliding mode control. The supercapacitor units are responsible for controlling the direct current (DC) bus, while the lithium-ion battery plays a role in balancing power distribution on the common line. This study evaluates the potential benefits of integrating salp swarm algorithm techniques into the controller's performance. The findings of the study suggest combining metaheuristic optimization methods with integral sliding mode control. Can lead to improvements in power quality. The proposed management algorithm not only efficiently allocates power resources but also safeguards them, ultimately ensuring a consistent and high-quality power supply for the electric vehicle.
KW - Electric vehicle
KW - Energy management
KW - Hybrid storage system
KW - Integral sliding mode control
KW - Optimization
KW - Salp swarm algorithm
UR - http://www.scopus.com/inward/record.url?scp=85206159042&partnerID=8YFLogxK
U2 - 10.11591/ijece.v14i6.pp6076-6085
DO - 10.11591/ijece.v14i6.pp6076-6085
M3 - Article
AN - SCOPUS:85206159042
SN - 2088-8708
VL - 14
SP - 6076
EP - 6085
JO - International Journal of Electrical and Computer Engineering
JF - International Journal of Electrical and Computer Engineering
IS - 6
ER -