Bidirectional DC-DC optimal control method based on time-varying weight model prediction
The bidirectional DC-DC converter is a vital voltage conversion component in electric vehicles,and improving its response speed and robustness under dynamic load conditions holds is crucial for the low-voltage power supply system of electric vehicles.To overcome the challenge,this paper proposes a model predictive control(MPC)method based on fuzzy time-varying weights.First,a dynamic model of the converter is built by the state-space averaging method.Second,a converter(MPC)model is built based on the MPC principle to achieve voltage tracking.Based on our analysis of the impact of weight factors on voltage tracking performance under dynamic load conditions,a weight fuzzy regulator is designed to facilitate online weight adjustment.Finally,an experimental platform is developed by HM-cSPACE to validate the precision of the proposed method.Comparative assessments are made against traditional PI control and conventional MPC control within the Matlab/Simulink environment.Our results indicate the converter employing time-varying weight MPC exhibits superior dynamic performance and robustness when subjected to load variations,reducing the overshooting by 26.1%.The proposed method is of great significance in enhancing the optimal control of bidirectional DC-DC converters under dynamic load conditions.
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