Investigation of artificial neural networks for estimating important component temperatures in electric motors

基于模型的电机重要部件温度估计是近年来的一个重要研究课题。它用于在运行期间保护组件，因此是提高现代驱动系统热利用率的重要基础。通常，基于电动机模型的间接方法可用于检测温度敏感参数变化，从而观察工作温度。这种模型类别具有固有的缺点，即所谓的热点温度不能被估计，并且一些重要的发动机温度（例如，滚珠轴承）不能被检测到。另一方面，泵浦参数热网络（LPTN）经常被用来直接模拟温度分布。关于LPTN建模深度，存在广泛的范围。对于能够实时的模型，特别是抽象的方法被用来估计相关的电机温度与模型结构，这是尽可能紧凑，从而计算效率。这里的问题是，替代模型方法是否更适合于复杂系统中的温度估计的抽象访问。通过避免基于LPTN的微分方程，则可以独立于要建模的部件的数量来选择模型的自由度的数量。人工神经网络（Artificial Neural Networks，KNN）包括各种不同的黑箱模型，并且已经在语音和图像分析等领域得到了广泛的应用。这些在本申请中被完全忽略，因此申请人对KNN的基本适用性进行了初步的初步调查。这里可以表明，使用长短期记忆或门控递归单元的拓扑结构提供了有希望的估计精度。然而，它们仍然低于既定的直接和间接方法。在这项研究中，已经确定了许多开放的研究问题，例如超参数优化问题。这些是所考虑的KNN拓扑的高级配置参数（例如，隐藏层的数量或每层神经元的数量）或所使用的训练算法（例如，KNN权重的初始化）。因此，该项目旨在系统地研究KNN用于电动机温度估计，从而开发通用方法和过程链，以便项目结果可以直接转移到相关的技术系统，例如电池或电力电子转换器。

The model-based estimation of important component temperatures in electric motors is an important research subject in recent years. It is used for component protection during run-time and thus forms an important basis for increasing the degree of thermal utilization regarding modern drive systems. Typically, indirect methods based on electrical motor models can be used to detect temperature-sensitive parameter changes and thus to observe the operating temperature. This model class has the inherent disadvantage that the so-called hot-spot temperature cannot be estimated, and that some important engine temperatures (e.g., ball bearings) cannot be detected. On the other hand, pumped parameter thermal networks (LPTN) are frequently used to directly model the temperature distribution. There is a wide range with regard to the LPTN modeling depth. For real-time capable models, particularly abstracted approaches are used to estimate the relevant motor temperatures with a model structure that is as compact as possible and thus computationally efficient. The question here is whether alternative model approaches are no better suited for empirical-abstract access to temperature estimation within complex systems. By avoiding LPTN-based differential equations, it is then possible in to select the number of degrees of freedom of the model independently of the number of components to be modeled. As a result, the estimation accuracy against abstract LPTN approaches could be further increased with a reasonable additional calculation effort.Artificial neural networks (KNN) comprise a wide range of different black-box models and already find a wide range of applications, e.g. in speech and image analysis. These have been completely neglected for the present application, so that the applicant has carried out an initial preliminary investigation into the basic suitability of KNN. Here it could be shown that topologies using long-short-term-memories or gated recurrent units provide promising estimation accuracy. However, they are still below the established direct and indirect methods. In this investigation, numerous open research questions have been identified, e.g. the problem of hyper-parameter optimization. These are superordinate configuration parameters of the considered KNN topology (for example, the number of hidden layers or number of neurons per layer) or the training algorithm used (for example, initialization of the KNN weights). This project is therefore aimed at the systematic investigation of KNN for the temperature estimation in electric motors, whereby a general methodological and process chain is developed so that the project results can be directly transferred to related technical systems, e.g. to batteries or power-electronic converters.