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

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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.