Switched Reluctance Machine Torque Ripple Suppression Strategy Based on 2D Data Interpolation
Fu Qianrao1, Guo Jixuan2, Song Jiahe2, Ge Lefei2,3
1. School of Management Xi’an University of Architecture and Technology Xi’an 710055 China; 2. School of Automation Northwestern Polytechnical University Xi’an 710129 China; 3. Shenzhen Research Institute of Northwestern Polytechnical University Shenzhen 518057 China
Abstract：The switched reluctance machine (SRM) has become one of the most competitive generator/ starter candidates in the field of electric vehicles and multi-electric aircraft. However, the torque ripple caused by the double-bump structure limits its applications. Realizing effective suppression of the torque ripple has become a research hotspot. Based on the accurate prediction model, model predictive control (MPC) can predict the variation of the target variable under all possible control signals and optimize the control signals online based on a predefined control law. However, the existing research only selects the optimal control signal within a limited set of control signals, which limits the suppression effect and does not fully utilize the flexibility of MPC. Moreover, the artificial setting of the parameters in the cost function affects the effectiveness of the MPC strategy. Thus, this paper proposes a torque ripple suppression strategy for the SRMs based on two-dimensional linear interpolation. Firstly, PWM control signals are used to replace the three switching states in the conventional predictive control. The predicted optimal control signal can smooth the current and flux-linkage more flexibly under the same calculation frequency, which effectively enhances the accuracy of torque control. Latin hypercube sampling is proposed to divide the duty cycle in the range [-1, 1] into n strata, and one random duty cycle is drawn within each stratum to compose the predicted duty cycle scheme. To convert the predicted optimal duty cycle into the corresponding switching tube control signals, a PWM modulation method for the asymmetric half-bridge power converter is proposed. Then, the operating region is divided into single-phase and commutation regions by online operating region adjustment, and different predictive control methods are used for each region. In the single-phase region, the deadbeat predictive control calculates the optimal duty cycle for the conduction phase to follow the phase torque reference. In the commutation region, the MPC predicts the optimal duty cycle for the incoming and outgoing phases. The expected duty cycle scheme is determined based on the SRM operation state, and the corresponding predicted torque is obtained using the prediction model. A two-dimensional linear interpolation method is proposed to expand the prediction dataset and find the optimal duty cycle that corresponds to the minimum predicted torque difference. Experimental verification of the torque ripple suppression effect is carried out on a three-phase 12/8-pole SRM experimental platform. The results show that in the commutation region, the proposed combined predictive control method produces more accurate control signals and prevents the occurrence of a large torque ripple due to the application of PWM control. In the single-phase region, according to the deadbeat predictive control’s fast response, the duty cycle of the conduction phase that minimizes the torque ripple in the next control cycle is accurately calculated, and the output torque can track the reference torque precisely. The proposed method, based on 2D data interpolation, can reduce torque ripple by 40-50% while causing almost no efficiency loss. The proposed method has the following advantages. (1) By applying PWM signals instead of the conventional finite-set predictive signals, the output optimal control signals are enriched, and the current and torque variations are smooth. (2) The proposed two-dimensional interpolation optimization, instead of the conventional cost function, not only realizes the application of PWM signals in MPC but also avoids the complicated and complex process of parameter tuning.
付倩娆, 郭继轩, 宋佳赫, 葛乐飞. 基于二维数据插值的开关磁阻电机转矩脉动抑制策略[J]. 电工技术学报, 2025, 40(24): 7949-7957.
Fu Qianrao, Guo Jixuan, Song Jiahe, Ge Lefei. Switched Reluctance Machine Torque Ripple Suppression Strategy Based on 2D Data Interpolation. Transactions of China Electrotechnical Society, 2025, 40(24): 7949-7957.
[1] Sayed E, Abdalmagid M, Pietrini G, et al.Review of electric machines in more-/ hybrid-/ turbo-electric aircraft[J]. IEEE Transactions on Transportation Electrification, 2021, 7(4): 2976-3005. [2] 于丰源, 陈昊, 王星, 等. 不同定子绕组结构双定子轴向磁通开关磁阻电机性能对比研究[J]. 电工技术学报, 2024, 39(24): 7728-7741, 7776. Yu Fengyuan, Chen Hao, Wang Xing, et al.Com- parative study on the double stator axial flux switched reluctance motors with different winding structures[J]. Transactions of China Electrotechnical Society, 2024, 39(24): 7728-7741, 7776. [3] Ge Lefei, Ralev I, Klein-Hessling A, et al.A simple reluctance calibration strategy to obtain the flux- linkage characteristics of switched reluctance machines[J]. IEEE Transactions on Power Electronics, 2019, 35(3): 2787-2798. [4] 韩国强, 韩颖, 贾政, 等. 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, et al.Model- free predictive current control method for switched reluctance motor with ultralocal model[J]. Transa- ctions of China Electrotechnical Society, 2025, 40(18): 5931-5944. [5] Fang Gaoliang, Scalcon F P, Xiao Dianxun, et al.Advanced control of switched reluctance motors (SRMs): a review on current regulation, torque control and vibration suppression[J]. IEEE Open Journal of the Industrial Electronics Society, 2021, 2: 280-301. [6] Cheok A D, Fukuda Y.A new torque and flux control method for switched reluctance motor drives[J]. IEEE Transactions on Power Electronics, 2002, 17(4): 543-557. [7] 陈飞, 瞿遂春, 邱爱兵, 等. 一种改进的开关磁阻电动机直接转矩控制方法[J]. 微特电机, 2018, 46(5): 66-69. Chen Fei, Qu Suichun, Qiu Aibing, et al.An improved direct torque control method for switched reluctance motor[J]. Small & Special Electrical Machines, 2018, 46(5): 66-69. [8] 任浩天, 甘醇, 曲荣海, 等. 基于电流转矩协同控制的SRM新型直接瞬时转矩控制策略[J]. 中国电机工程学报, 2024, 44(19): 7794-7806. Ren Haotian, Gan Chun, Qu Ronghai, et al.Current- torque coordinated control based direct instantaneous torque control strategy for switched reluctance motor[J]. Proceedings of the CSEE, 2024, 44(19): 7794-7806. [9] 杨帆, 陈昊, 李晓东, 等. 一种优化开关磁阻电机换相区控制策略的高效率转矩分配函数[J]. 电工技术学报, 2024, 39(6): 1671-1683. Yang Fan, Chen Hao, Li Xiaodong, et al.An efficient torque sharing function for optimizing the com- mutation zone control strategy of switched reluctance motors[J]. Transactions of China Electrotechnical Society, 2024, 39(6): 1671-1683. [10] Ge Lefei, Fan Zizhen, Huang Jiale, et al.Model predictive control of switched reluctance machines with online torque sharing function based on optimal flux-linkage curve[J]. IEEE Transactions on Trans- portation Electrification, 2024, 10(3): 4990-5001. [11] Zhang Yanqing, Yin Zhonggang, Li Wei, et al.Finite control set model predictive torque control using sliding model control for induction motors[J]. CES Transactions on Electrical Machines and Systems, 2021, 5(3): 262-270. [12] 韩硕, 张勇军, 肖雄, 等. 面向异步电机模型预测直接转矩控制的自适应谐波消除方法[J]. 电工技术学报, 2025, 40(4): 1078-1089. Han Shuo, Zhang Yongjun, Xiao Xiong, et al.Adaptive harmonic elimination for model predictive direct torque control of asynchronous motor[J]. Transactions of China Electrotechnical Society, 2025, 40(4): 1078-1089. [13] 兰志勇, 罗杰, 李延昊, 等. 基于快速选择表的永磁同步电机模型预测转矩控制[J]. 电工技术学报, 2023, 38(21): 5749-5757. Lan Zhiyong, Luo Jie, Li Yanhao, et al.Model prediction torque control for permanent magnet synchronous motor based on the fast selection table[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5749-5757. [14] Peyrl H, Papafotiou G, Morari M.Model predictive torque control of a switched reluctance motor[C]//2009 IEEE International Conference on Industrial Technology, Churchill, VIC, Australia, 2009: 1-6. [15] Ge Lefei, Zhong Jixi, Huang Jiale, et al.A novel model predictive torque control of SRMs with low measurement effort[J]. IEEE Transactions on Indu-strial Electronics, 2023, 70(4): 3561-3570. [16] Fang Gaoliang, Ye Jin, Xiao Dianxun, et al.Computational-efficient model predictive torque control for switched reluctance machines with linear- model-based equivalent transformations[J]. IEEE Transactions on Industrial Electronics, 2022, 69(6): 5465-5477. [17] Song Shoujun, Liu Jialiang, Zhao Yong, et al.High- dynamic four-quadrant speed adjustment of switched reluctance machine with torque predictive control[J]. IEEE Transactions on Industrial Electronics, 2021, 69(8): 7733-7743. [18] Kouro S, Cortes P, Vargas R, et al.Model predictive control: a simple and powerful method to control power converters[J]. IEEE Transactions on Industrial Electronics, 2009, 56(6): 1826-1838. [19] Rodriguez J, Kazmierkowski M P, Espinoza J R, et al.State of the art of finite control set model predictive control in power electronics[J]. IEEE Transactions on Industrial Informatics, 2013, 9(2): 1003-1016. [20] Ge Lefei, Guo Jixuan, Mao Shuai, et al.A composite model predictive control method of SRMs with PWM-based signal for torque ripple suppression[J]. IEEE Transactions on Transportation Electrification, 2024, 10(2): 2469-2478. [21] Song Shoujun, Ge Lefei, Ma Shaojie, et al.Accurate measurement and detailed evaluation of static elec- tromagnetic characteristics of switched reluctance machines[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(3): 704-714. [22] Fang Gaoliang, Ye Jin, Xiao Dianxun, et al.Low- ripple continuous control set model predictive torque control for switched reluctance machines based on equivalent linear SRM model[J]. IEEE Transactions on Industrial Electronics, 2022, 69(12): 12480-12495.
2025, Vol. 40 
