Model Predictive and Iterative Learning Control based Hybrid Control Method for Hybrid Energy Storage System

Author(s):  
Xibeng Zhang ◽  
Benfei Wang ◽  
Don Gamage ◽  
Abhisek Ukil
2021 ◽  
Vol 13 (14) ◽  
pp. 7682
Author(s):  
Min-Fu Hsieh ◽  
Po-Hsun Chen ◽  
Fu-Sheng Pai ◽  
Rui-Yang Weng

This paper presents a C-rate control method for a battery/supercapacitor (SC) hybrid energy storage system (HESS) to enhance the life cycle of the battery in electric vehicles (EVs). The proposed HESS provides satisfactory power for dynamic movements of EVs (e.g., acceleration or braking) while keeping the battery current within a secure level to prevent it from degradation. The configurations of conventional HESSs are often complex due to the two energy storages and their current/voltage sensing involved. Therefore, in this paper, a simple current-sensing scheme is utilized and the battery is directly treated as a controlled variable to help the battery output current remain stable for different load conditions. While the proposed circuit requires only one current feedback signal, neither the SC nor load current sensors are needed, and the circuit design is thus significantly simplified. Both simulation and experimental results validated the effectiveness of the proposed HESS operating in conjunction with the motor drive system. The proposed method aims at fully utilizing recycled energy and prolonging battery lifespan.


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