Abstract：Placing a dual active bridge (DAB) DC-DC converter between the power battery and the motor controller of an electric vehicle can adjust the DC link voltage according to motor speed, improving the energy conversion efficiency of the motor drive system. However, it also brings the following challenges. The wide range variation of DC bus voltage in the motor drive system can cause mismatched input and output voltage of the DAB converter, resulting in problems such as a sharp increase in current stress, loss of soft switches, and increased losses, ultimately reducing system efficiency. Moreover, for the rapid release and recovery characteristics of enormous energy during electric vehicle operation, DAB converters need to withstand high voltage and current impacts, maintaining a fast dynamic response speed. Therefore, the current stress characteristics and optimization methods of DAB converters under dynamic DC link voltage are analyzed, and the model predictive control method is studied. Accordingly, a control strategy that combines model prediction control and current stress optimization is proposed. The current stress optimization control is embedded into the model predictive control for improving the steady and dynamic performance of the system and reducing the current stress. In addition, the discrete state space model of the output capacitor is established according to the circuit topology. Then, the model predictive control strategy is used to calculate the future output voltage in the prediction time domain according to the current output voltage value of the DAB converter. Furthermore, a cost function is constructed to evaluate the output performance of the DAB converter, and a rolling optimization method is used to solve the cost function and calculate the optimized phase shift control combination variable. Finally, the internal and external phase shift ratios D1 and D2 are calculated. An error correction method based on output feedback is proposed to suppress the adverse effects of model parameter mismatch on model predictive control. With the error correction method, the proposed hybrid control of model prediction and current stress optimization has the advantage of parameter insensitivity and good dynamic performance under different inductance conditions, improving the parameter robustness. A micro prototype experimental platform for the DAB converter and motor drive system is built in the laboratory. The proposed control strategy has good steady-state characteristics and can effectively reduce current stress, almost identical to other optimization control strategies. Regarding dynamic performance, the stability time and overshoot can be almost ignored, and the output voltage is unaffected by transient changes such as sudden load change. The experimental results show that the proposed control strategy has a simple imple- mentation process, strong flexibility, and adaptability, which can meet the requirements of variable DC bus voltage operation in electric vehicle motor drive systems. It provides a theoretical basis for the design and control of motor controllers featuring a DAB converter positioned before the DC link in EV applications.
关维德, 李涛, 钟健, 王旭红, 夏向阳. 电机控制器直流侧前置双有源桥DC-DC变换器的模型预测与应力优化混合控制[J]. 电工技术学报, 2024, 39(12): 3787-3801.
Guan Weide, Li Tao, Zhong Jian, Wang Xuhong, Xia Xiangyang. Hybrid Control of Model Prediction and Current Stress Optimization for Dual Active Bridge DC-DC Converter in Motor Drive Systems. Transactions of China Electrotechnical Society, 2024, 39(12): 3787-3801.
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2024, Vol. 39 
