电动汽车双向DC-DC变换器约束模型预测控制研究

doi:10.19595/j.cnki.1000-6753.tces.L80683

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Abstract In order to improve the response speed and reliability of bidirectional DC-DC converters in electric vehicles, a constrained model predictive control (MPC) based on particle swarm optimization was proposed to apply to interleaved parallel bidirectional DC-DC converter. Based on the system transfer function, a predictive model for bidirectional DC-DC converter in Buck mode was established. Then, the principle of the constraint model predictive control algorithm was analyzed, and added constraints to control variables. The particle swarm optimization algorithm was used to solve the constraint prediction control optimization problem so that the speed solution was improved. The simulation was carried out with Matlab/Simulink, and the experimental platform was built. The simulation results and experimental results of constraint prediction control, PI control and unconstrained predictive control were analyzed and compared. Simulation and experimental results show that the converter with constrained model predictive control has better dynamic response performance and steady state performance, and the algorithm is feasible and effective.

Key words： Bidirectional DC-DC converter electric vehicles constrained model predictive control particle swarm optimization

Received: 08 July 2018 Published: 15 February 2019

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	Articles by authors
	Xiao Zhiming
	Chen Qihong
	Zhang Liyan

Cite this article:

Xiao Zhiming,Chen Qihong,Zhang Liyan. Constrained Model Predictive Control for Bidirectional DC-DC Converter of Electric Vehicles[J]. Transactions of China Electrotechnical Society, 2018, 33(zk2): 489-498.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L80683 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/Izk2/489

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