Energy Storage Science and Technology ›› 2021, Vol. 10 ›› Issue (6): 2046-2052.doi: 10.19799/j.cnki.2095-4239.2021.0210

• Special issue of hydrogen energy and fuel cell • Previous Articles     Next Articles

Model predictive control for bidirectional DC-DC converter of hydrogen fuel vehicles

Siyan LIU1(), Bihua HU2   

  1. 1.Photovoltaic System Control and Optimization of Hunan Province Engineering Laboratory
    2.School of Automation and Electronic Information, Xiangtan University, Xiangtan 411100, Hunan, China
  • Received:2021-05-13 Revised:2021-06-16 Online:2021-11-05 Published:2021-11-03
  • Contact: Siyan LIU E-mail:690324828@qq.com

Abstract:

The bidirectional interleaved parallel DC-DC (BDC) converter is an important device for hydrogen fuel cell vehicle power supply reliability and energy recovery. The BDC converter experiences slow response speed, low stability, and output current ripple when using traditional control methods. Aiming at the above problems, a constrained model predictive current control is proposed. First, mathematical models for the various working modes of the BDC converter are established, and an improved current prediction model for the various working modes of the BDC converter based on the vector working principle is constructed. The cost function is then optimized to solve the problem of frequent switch jitter in the model predictive control process, and the control variable increment is added to the constraint condition; to solve the output current ripple problem, the model predictive control strategy is improved by calculating the switch duty cycle online. Finally, calculate the vector action time and create the cost function to achieve the control objective. The traditional current control method's response time and current ripples are 0.1 s and 5 A, respectively. In contrast, the improved MPC model predictive control response time and current ripples are 0.02 s and 1.5 A. The experimental and simulation comparison results show that Model predictive current control with constraints has a better dynamic response and stable performance, which verifies the algorithm's effectiveness.

Key words: hydrogen fuel cell, electric vehicle, bidirectional DC-DC converter, model predictive control, duty cycle

CLC Number: