Model Predictive Control of Dual Active Bridge Converter Based on the Lookup Table Method

Author(s):  
Guoqing Gao ◽  
Wanjun Lei ◽  
Yao Cui ◽  
Kai Li ◽  
Xiufang Hu ◽  
...  
2020 ◽  
Vol 35 (8) ◽  
pp. 8624-8637 ◽  
Author(s):  
Linglin Chen ◽  
Lyuyi Lin ◽  
Shuai Shao ◽  
Fei Gao ◽  
Zhenyu Wang ◽  
...  

2016 ◽  
Vol 63 (9) ◽  
pp. 5558-5568 ◽  
Author(s):  
Shajjad Chowdhury ◽  
Patrick W. Wheeler ◽  
Chris Gerada ◽  
Chintan Patel

2020 ◽  
Vol 35 (2) ◽  
pp. 1957-1966 ◽  
Author(s):  
Linglin Chen ◽  
Shuai Shao ◽  
Qian Xiao ◽  
Luca Tarisciotti ◽  
Patrick W. Wheeler ◽  
...  

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2134 ◽  
Author(s):  
Lujun Wang ◽  
Jiong Guo ◽  
Chen Xu ◽  
Tiezhou Wu ◽  
Huipin Lin

In order to solve the problem of which the dynamic response of a supercapacitor (SC) is limited due to the mismatch dynamic characteristics between the DC/DC converter and supercapacitor in an energy storage system, this paper proposes a hybrid model predictive control strategy based on a dual active bridge (DAB). The hybrid model predictive control model considers the supercapacitor and DAB in a unified way, including the equivalent series resistance and capacitance parameters of the SC. The method can obtain a large charging and discharging current of the SC, thereby not only improving the overall response speed of the system, but also expanding the actual capacity utilization range of the SC. The simulation results show that compared with the model prediction method of the dual active bridge converter, the proposed control method can effectively improve the overall response speed of the system, which can be improved by at least 0.4 ms. In addition, the proposed method increases the actual upper limit of the SC voltage, reduces the actual lower limit of the SC voltage, and then expands the actual capacity utilization range of the SC by 18.63%. The proposed method has good application prospects in improving the dynamic response performance of energy storage systems.


Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3317 ◽  
Author(s):  
Alecksey Anuchin ◽  
Galina L. Demidova ◽  
Chen Hao ◽  
Alexandr Zharkov ◽  
Andrei Bogdanov ◽  
...  

A problem of the switched reluctance drive is its natural torque pulsations, which are partially solved with finite control set model predictive control strategies. However, the continuous control set model predictive control, required for precise torque stabilization and predictable power converter behavior, needs sufficient computation resources, thus limiting its practical implementation. The proposed model predictive control strategy utilizes offline processing of the magnetization surface of the switched reluctance motor. This helps to obtain precalculated current references for each torque command and rotor angular position in the offline mode. In online mode, the model predictive control strategy implements the current commands using the magnetization surface for fast evaluation of the required voltage command for the power converter. The proposed strategy needs only two lookup table operations requiring very small computation time, making instant execution of the whole control system possible and thereby minimizing the control delay. The proposed solution was examined using a simulation model, which showed precise and rapid torque stabilization below rated speed.


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