Abstract：In the dual pressure of energy resource scarcity and environmental protection, the new energy electric vehicles have become a research focus in the automotive field. With the increasing demand for control performance, electric vehicles with motor drive systems are receiving more and more attention. However, due to the limitation of rare earth reserves on the development of electric vehicle drive motors, the research on SRM drive systems for electric vehicles without rare earth plays an irreplaceable role in promoting the healthy and sustainable development of the new energy vehicle industry. Since the SRM has the advantages of a simple structure, low cost, flexible control methods, and strong fault tolerance, it has competitiveness in the field of electric vehicles. The conventional model predictive current control method has the following disadvantages. (1) The predictive strategy relies on the motor model. If the parameters of the motor are different from the normal value, the control performance will deteriorate. (2) For the motors with highly nonlinear characteristics, such as SRM, it is not easy to realize the predictive control schemes. A high control performance control strategy is investigated to improve the control performance of the SRM drive system. A novel model-free predictive current control method for SRM with the ultralocal model is proposed to reduce the effect of the system parameters and disturbance. The ultralocal model of SRM is established to calculate the predicted current value for the next moment. A linear extended state observer is designed to estimate disturbances, and the stability conditions of the observer are based on the Jury criterion. Based on the three basic operating modes of the power converter, a simplified predictive control set is adopted. Then, the predicted reference current for the next moment is calculated. The optimal switching vector for the next moment is determined based on the relationship between the predicted and reference currents. The model-free predictive control of SRM is achieved with the double closed-loop control strategy. Simulation models and experimental platforms are built. The simulation model mainly includes the electro-mechanical equation module, the asymmetric half-bridge power converter module, the control signal generation module, and the phase winding module. The experimental setup is established, and the three-phase 12/8 SRM is tested. The power transistor and diode types are IKW75N60T and IDW75E60FKSA1, respectively. The control chip is the TMS320F28335. The sampling chip is selected as the synchronous sampling chip AD7606 to reduce the delay error in the sampling process. The tested conditions include the stable operation case, the speed variation case, the load torque variation case, and the parameter mismatch case. The simulation and experimental results show that the proposed model-free predictive current control method has good steady-state and dynamic performance. The following conclusions can be drawn. (1) The proposed method dynamically adjusts the optional switch vectors in each subregion, reducing the computational workload. (2) The proposed method has better anti-interference ability when changing the reference speed, load torque, motor parameter, and motor supply voltage. (3) The proposed method is suitable for light load conditions at low speed and heavy load conditions at high speed. (4) The proposed method is easy to integrate with strategies like speed closed-loop control and online implementation.
韩国强, 韩颖, 贾政,朱惠敏,于东升. 基于超局部模型的开关磁阻电机无模型预测电流控制方法[J]. 电工技术学报, 2025, 40(18): 5931-5944.
Han Guoqiang, Han Ying, Jia Zheng, Zhu Huimin, Yu Dongsheng. Model-Free Predictive Current Control Method for Switched Reluctance Motor with Ultralocal Model. Transactions of China Electrotechnical Society, 2025, 40(18): 5931-5944.
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