On-Line Temperature Estimation of Permanent Magnet Motor Based on Lumped Parameter Thermal Network Method
Shi Wei1, Luo Kaichuan1, Zhang Zhouyun2
1. College of Urban Rail Transit Shanghai University of Engineering Science Shanghai 201620 China; 2. Shanghai Electric Drive Co. Ltd Shanghai 201806 China
Abstract The online temperature estimation of permanent magnet motors (PMM) can prevent motor damage caused by over temperature, especially reduce the risk of irreversible demagnetization of permanent magnet and improve the safety of PMM. For traditional online temperature estimation of lumped parameter thermal network (LPTN), the influence of LPTN topology with different scales on the speed and accuracy of on-line estimation has yet to be deeply studied. At the same time, the thermal resistance unit nonlinearity and the error accumulation from loss calculation generate the deviation between the estimated temperature and the actual value. Therefore, after comparing the low-order gray box LPTN models of PMM with different nodes, this paper proposes the weighted multi-innovation strength tracking extended Kalman filter (WMI-STEKF) algorithm to carry out on-line parameter identification and temperature estimation with high accuracy under variable working conditions. Firstly, the LPTN gray box models of PMM with different nodes are established. Next, the experimental temperature platform of variable condition temperature of PMM is built. Based on the experimental temperature data, the models are compared and analyzed. The optimal LPTN model for online temperature estimation of PMM is obtained. Third, the time-varying fading factor is introduced into the extended Kalman filtering algorithm to adjust the real-time gain matrix to improve the robustness of the model and the accuracy of the extended Kalman filtering algorithm identification. At the same time, the residual scalar in the extended Kalman filtering algorithm is extended to the innovation matrix to improve the adaptability of the system to nonlinear systems. WMI-STEKF algorithm is used in on-line parameter identification and temperature estimation. The grey box thermal network models with four different node models are evaluated according to the experimental temperature data. The five-node model has the best accuracy, convergence speed, and stability. The average error and maximum error of the temperature estimation results are 3.53 ℃ and 5.87 ℃, respectively, lower than other node number models. In the online parameter identification and temperature estimation of the LPTN model using the WMI-STEKF algorithm, six consecutive working conditions of 45 kW, 3 500 r/min; 45 kW, 3 750 r/min; 45 kW, 4 000 r/min; 45 kW, 4 500 r/min; 16 kW, 1 000 r/min; 32 kW, 2 000 r/min are implemented. After temperature identification, the error between the estimated and measured permanent magnet temperatures is within 3 ℃. The following conclusions are drawn: (1) The low-order gray five-node LPTN model can meet the needs of online temperature estimation. Through system identification and experimental data, the proposed model is the optimal online estimation model for experimental motors under different working conditions and quantitative temperature estimation index evaluation. (2) Based on multi-innovation theory and strong tracking extended Kalman filter algorithm, the accumulated error in the temperature estimation process caused by model simplification and thermal resistance change is effectively compensated under different working conditions. This algorithm improves the applicability in the strongly nonlinear system of motor thermal model and the robustness of process parameter changes in parameter identification. It has high accuracy in online temperature identification.
Shi Wei,Luo Kaichuan,Zhang Zhouyun. On-Line Temperature Estimation of Permanent Magnet Motor Based on Lumped Parameter Thermal Network Method[J]. Transactions of China Electrotechnical Society, 2023, 38(10): 2686-2697.
[1] 李晓华, 赵容健, 田晓彤, 等. 逆变器供电对电动汽车内置式永磁同步电机振动噪声特性影响研究[J]. 电工技术学报, 2020, 35(21): 4455-4464. Li Xiaohua, Zhao Rongjian, Tian Xiaotong, et al.Study on vibration and noise characteristics of interior permanent magnet synchronous machine for electric vehicles by inverter[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4455-4464. [2] Shi Wei, Zhou Xuan.Online estimation method for permanent magnet temperature of high-density per- manent magnet synchronous motor[J]. IEEJ Transa- ctions on Electrical and Electronic Engineering, 2020, 15(5): 751-756. [3] 张业成, 刘国海, 陈前. 基于电流波动特征的永磁同步电机匝间短路与局部退磁故障分类诊断研究[J]. 电工技术学报, 2022, 37(7): 1634-1643, 1653. Zhang Yecheng, Liu Guohai, Chen Qian.Dis- crimination of interturn short-circuit and local demagnetization in permanent magnet synchronous motor based on current fluctuation characteristics[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1634-1643, 1653. [4] 朱元, 肖明康, 陆科, 等. 电动汽车永磁同步电机转子温度估计[J]. 电机与控制学报, 2021, 25(6): 72-81. Zhu Yuan, Xiao Mingkang, Lu Ke, et al.Rotor temperature estimation for permanent magnet syn- chronous motors in electric vehicles[J]. Electric Machines and Control, 2021, 25(6): 72-81. [5] Reigosa D D, Fernandez D, Tanimoto T, et al.Permanent-magnet temperature distribution estimation in permanent-magnet synchronous machines using back electromotive force harmonics[J]. IEEE Transa- ctions on Industry Applications, 2016, 52(4): 3093-3103. [6] Reigosa D D, Fernandez D, Yoshida H, et al.Permanent-magnet temperature estimation in PMSMs using pulsating high-frequency current injection[J]. IEEE Transactions on Industry Applications, 2015, 51(4): 3159-3168. [7] 刘平, 王鑫, 孙千志, 等. 永磁同步电机定子绕组温度估计的信号注入策略优化[J]. 电机与控制学报, 2019, 23(11): 18-26. Liu Ping, Wang Xin, Sun Qianzhi, et al.Signal injection strategy optimization of stator winding temperature estimation for permanent magnet syn- chronous motor[J]. Electric Machines and Control, 2019, 23(11): 18-26. [8] Gaona D, Wallscheid O, Böcker J.Fusion of a lumped-parameter thermal network and speed-dependent flux observer for PM temperature estimation in synchronous machines[C]//IEEE Southern Power Electronics Conference (SPEC), Puerto Varas, Chile, 2018: 1-6. [9] Camilleri R, Howey D A, McCulloch M D. Predicting the temperature and flow distribution in a direct oil-cooled electrical machine with segmented stator[J]. IEEE Transactions on Industrial Electronics, 2016, 63(1): 82-91. [10] Liang Dawei, Zhu Z Q, Zhang Yafeng, et al.A hybrid lumped-parameter and two-dimensional analytical thermal model for electrical machines[J]. IEEE Transactions on Industry Applications, 2021, 57(1): 246-258. [11] 师蔚, 靳荣华. 基于扩展卡尔曼滤波的永磁电机热网络参数辨识[J]. 电机与控制学报, 2020, 24(12): 106-112. Shi Wei, Jin Ronghua.Thermal network parameters identification of permanent magnet motor based on extended Kalman filter[J]. Electric Machines and Control, 2020, 24(12): 106-112. [12] Wang Wenbo, Zhou Yubin, Chen Yangsheng.Investigation of lumped-parameter thermal model and thermal parameters test for IPMSM[C]//17th Inter- national Conference on Electrical Machines and Systems, Hangzhou, China, 2015: 3246-3252. [13] Wallscheid O, Böcker J.Global identification of a low-order lumped-parameter thermal network for permanent magnet synchronous motors[J]. IEEE Transactions on Energy Conversion, 2016, 31(1): 354-365. [14] 骆凯传, 师蔚, 张舟云. 基于温度实验的永磁同步电机损耗分离方法[J]. 电工技术学报, 2022, 37(16): 4060-4073. Luo Kaichuan, Shi Wei, Zhang Zhouyun.Method of loss separation of permanent magnet synchronous motor based on temperature experiment[J]. Transa- ctions of China Electrotechnical Society, 2022, 37(16): 4060-4073. [15] 巫春玲, 胡雯博, 孟锦豪, 等. 基于最大相关熵扩展卡尔曼滤波算法的锂离子电池荷电状态估计[J]. 电工技术学报, 2021, 36(24): 5165-5175. Wu Chunling, Hu Wenbo, Meng Jinhao, et al.State of charge estimation of lithium-ion batteries based on maximum correlation-entropy criterion extended Kalman filtering algorithm[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5165-5175. [16] 刘芳, 马杰, 苏卫星, 等. 基于自适应回归扩展卡尔曼滤波的电动汽车动力电池全生命周期的荷电状态估算方法[J]. 电工技术学报, 2020, 35(4): 698-707. Liu Fang, Ma Jie, Su Weixing, et al.State of charge estimation method of electric vehicle power battery life cycle based on auto regression extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(4): 698-707. [17] Wu Pengfei, Shi Zhangsong, Yan Penghao.Improved EKF-SLAM algorithm of unmanned helicopter auto- nomous landing on ship[C]//37th Chinese Control Conference, Wuhan, China, 2018: 5287-5292. [18] Cheng Yan, Chang Qing.A carrier tracking loop using adaptive strong tracking Kalman filter in GNSS receivers[J]. IEEE Communications Letters, 2020, 24(12): 2903-2907. [19] 丁锋. 系统辨识(6): 多新息辨识理论与方法[J]. 南京信息工程大学学报(自然科学版), 2012, 4(1): 1-28. Ding Feng.System identification. part F: multi- innovation identification theory and methods[J]. Journal of Nanjing University of Information Science & Technology (Natural Science Edition), 2012, 4(1): 1-28. [20] Han Yiyang, Ding Jie, Chen Jiazhong, et al.SOC estimation method for lithium-ion batteries: extended Kalman filter with weighted innovation[C]//Chinese Control and Decision Conference, Nanchang, China, 2019: 5074-5078.
2023, Vol. 38 
