The impacts of control systems on hybrid energy storage systems in remote DC-Microgrid system: A comparative study between PI and super twisting sliding mode controllers

2021 ◽  
pp. 103586
Author(s):  
Hartani Mohamed Amine ◽  
Al Kouzbary Mouaz ◽  
Hamouda messaoud ◽  
Abdelkhalek Othmane ◽  
Mekhilef Saad
Keyword(s):  
Energy Storage ◽  
Comparative Study ◽  
Control Systems ◽  
Sliding Mode ◽  
Storage Systems ◽  
Dc Microgrid ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
System A ◽  
Sliding Mode Controllers

scholarly journals A Comparative Study of Adaptive Filtering Strategies for Hybrid Energy Storage Systems in Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3373
Author(s):  
Hoai-Linh T. Nguyen ◽  
Bảo-Huy Nguyễn ◽  
Thanh Vo-Duy ◽  
João Pedro F. Trovão
Keyword(s):  
Energy Storage ◽  
Comparative Study ◽  
Adaptive Filtering ◽  
Storage Systems ◽  
Management Strategies ◽  
Energy Storage Systems ◽  
Vehicle Performance ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Vehicle Operation

Hybrid energy storage systems (HESSs) including batteries and supercapacitors (SCs) are a trendy research topic in the electric vehicle (EV) context with the expectation of optimizing the vehicle performance and battery lifespan. Active and semi-active HESSs need to be managed by energy management strategies (EMSs), which should be realized on real-time onboard platforms. A widely used approach is the filter-based EMS thanks to its simplicity and effectiveness. However, one question that always arises with these algorithms is how to determine the appropriate constant cut-off frequency. To tackle this challenge, this paper proposed three adaptive schemes for the filtering strategies based on the SC “ability” and evaluated their performance during the vehicle operation via an intensive comparative study. Offline simulation and experimental validation using signal hardware-in-the-loop (HIL) emulation showed that the proposed adaptive filtering EMS can reduce the battery rms current considerably. Specifically, the SC-energy-based, SOC-based, and voltage-based algorithms minimized the battery rms by up to 69%, 66%, and 64%, respectively, when compared to a pure battery EV in a fluctuating driving condition such as the urban Artemis cycle.

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