Robust Real-Time Thermal Modelling of High-Speed Permanent Magnet Synchronous Machine

随着汽车行业和能源行业电气化程度的提高，对电机的需求从未如此之大。因此，降低这些机器的成本和尺寸，同时最大限度地提高其功率能力至关重要。实现这些目标的一个主要机会是通过对关键电机部件（如端部绕组和永磁体）进行准确的实时热建模。这种模型将能够测量电机内的困难或难以接近的位置，而不需要昂贵的传感器。因此，给定机器的功率密度可以增加，因为由于机器内的温度不确定性而存在的大的安全裕度可以减小。此外，这可以使电机能够缩小尺寸，同时仍然满足给定应用所需的额定功率。 这个项目的动机是广泛地解决上述好处的实时热模型可以使;然而，它更确切地说是由目前缺乏一致性，鲁棒性，目前提出的方法在文献中的实施。对该主题的全面回顾概述了许多不同建模技术的缺点和优点，包括机器学习、降阶热网络、状态观测器和混合方法。虽然没有一种方法是最受欢迎的，但大多数方法都围绕着集总电容建模。 此外，在文献中发现的测试是基于严格的实验室条件下的数据集，通常具有简单的测试周期。如果将模型实施到现实世界的系统中，即在汽车环境中，这就带来了模型的鲁棒性和可行性问题。这一点得到了进一步加强，因为许多出版物依赖于预先记录的实验数据集进行机器学习，参数化和测试。最后，该实验数据集与文献中的许多其他数据集非常相似，使用相对低速（6000 rpm）的液体冷却电机，因此高速电机导致的内部现象可能尚未被捕获，并且其他冷却方法也不被理解。该项目的科学影响将使测试和汽车应用中的电机尺寸缩小。由于电机内的温度不确定性，这些机器目前受到较大热安全裕度的限制。此外，通过对机器内的温度进行建模，可以通过规避对难以接近的位置（例如，转子磁体）。最后，该项目旨在提高具有不同几何形状和冷却系统的机器建模的灵活性，减少与模型参数化相关的成本和时间，同时提供可靠和准确的结果。

As a result of increased electrification within the automotive industry and energy sector, the demands from electric machines have never been greater. Therefore, reducing the cost and size of these machines whilst maximising their power capabilities is crucial. A prime opportunity to achieve these targets is through accurate real-time thermal modelling of key motor components, such as the end windings and permanent magnets. Such a model would enable the measurement of difficult or inaccessible locations within the motor, without the need for expensive sensors. Therefore, the power density of a given machine could be increased as the large safety margin, which exists due to temperature uncertainties within the machine, could be reduced. Additionally, this could enable electric machines to be downsized, whilst still meeting the required power ratings for a given application. The motivation for this project is broadly to address the aforementioned benefits a real-time thermal model could enable; however, it is more precisely motivated by the current lack of agreement, robustness, and implementation of methods currently proposed within the literature. Comprehensive reviews of the topic outline drawbacks and benefits to many different modelling techniques, including machine learning, reduced order thermal networks, state observers, and hybrid approaches. Although no single method is yet to prevail as a favourite, most methods revolve around lumped capacitance modelling. Furthermore, testing found within the literature is based on datasets in strict laboratory conditions, often with simplistic test cycles. This brings into question the models' robustness and feasibility if implemented onto a real-world system, namely in automotive contexts. This is further reinforced as many publications rely on a pre-recorded experimental dataset for machine learning, parametrisation, and testing. Finally, this experimental dataset, much like many others in the literature, uses a relatively low speed (6000 rpm) liquid cooled motor, hence internal phenomena resulting from high-speed motors may have not been captured and other cooling methods are not understood. The scientific impact of this project will be to enable downsizing of electric machines in testing and automotive applications. These machines are currently restricted by large thermal safety margins due to temperature uncertainties within the motor. Additionally, by modelling the temperature within the machine, the cost of test bed systems can be reduced by circumventing the requirement for expensive sensors in poorly accessible locations (e.g., rotor magnets). Finally, this project seeks to increase the flexibility of modelling machines with differing geometry and cooling systems, reducing the cost and time associated with parameterising the model, whilst delivering reliable and accurate results.