感应电机电热灰盒模型及参数辨识

Marius Stender, O. Wallscheid, J. Böcker
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引用次数: 1

摘要

由于在电动汽车等扭矩控制应用中广泛使用感应电机,因此感应电机的精确建模和识别变得越来越重要。为了实现高精度,必须对包括热效应在内的几种非理想电机特性进行建模和识别。文献中大多数热模型使用的损耗模型与控制任务中考虑的电机模型分离,导致这些模型之间不一致。在这篇贡献中,开发了一个结合电-热模型并解决了其识别问题。因此,实现的通用驱动模型提供磁链,扭矩,损耗和温度估计。因此,该模型提供了三个主要驱动任务的信息:一般控制、运行策略和状态监测。通过在试验台记录的综合数据集,对模型参数进行了优化识别。在一个单独的测试集上,所提出的模型被验证可以估计电机产生的转矩,与标称转矩相关的均方根误差为0.4%,定子和转子的温度的均方根误差分别为1.0 K和1.1 K。
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Combined Electrical-Thermal Gray-Box Model and Parameter Identification of an Induction Motor
Precise modeling and identification of induction motors is becoming increasingly important due to the extensive use of these motors in torque-controlled applications, e.g., electric vehicles. To achieve high precision, several nonideal motor characteristics including thermal effects have to be modeled and identified. Most thermal models in the literature utilize a loss model which is separated from the motor model considered in the control task leading to inconsistencies between these models. In this contribution, a combined electrical-thermal model is developed and its identification is addressed. Hence, the achieved universal drive model delivers flux, torque, loss and temperature estimations. Thus, the model provides information for three main drive tasks: general control, operating strategy and condition monitoring. With a comprehensive data set recorded at the test bench, the model parameters are optimally identified. On a separate test set, the proposed model is validated to estimate the torque generated by the motor with a root-mean-square error of 0.4 % related to nominal torque as well as the temperatures in the stator and rotor with root-mean-square errors of 1.0 K and 1.1 K, respectively.
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