Autonomous parameter estimation for electric machines

电机的自主参数估计

• 批准号: 2602743
• 金额: --
• 依托单位: University of Bath
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2602743
• 关键词: Autonomous; parameter; estimation; electric; machines

Electric machines are becoming more prevalent in the automotive industry as they become the main propulsion system in road vehicles with the industry's shift towards emissions free mobility. With over 15% of new car sales being electric, being able to accurately characterise electric machines virtually is imperative for maximising their performance and efficiency. A key predictor of a model's ability to replicate transient behaviour is the accuracy of the parameters used to characterise the motor. Relying solely on the information and specifications provided by the manufacturer to create a robust model is impractical as they often only include information required for the machine's operation. The overarching aim of this work is to develop a procedure to automate the parameterisation of electric motor models for later use in the vehicle development process.There are mant potential use cases for motor models, and many motor architectures of interest. In each combination of use case and motor architecture, the appropriate motor model structure is expected to differ. Typically, the level of spatial and temporal resolution will increase when more insight into detailed motor performance is needed. Once a model structure is defined, the data required to parameterise and validate this model can be defined. Then, the experiments necessary to generate this data, along with the instrumentation required can be defined.Focusing on the model development of electric machines, this project aims to create an end to end workflow between model and data to increase model accuracy and adaptability to new units under test.The work will explore the potential for a general motor model and parameterisation procedure that is compatible with all the likely motor topologies of interest: flux switching, induction, and synchronous motor architectures. It will focus on implementing an autonomous parameter characterisation process, and on streamlining the experimental procedure behind the collection of data required for the parameterisation of an electric machine. Electric machine architectures vary enough to require bespoke motor models to simulate it's behaviour. Over the course of the first 6 months of the project, a model of a synchronous motor will be created with the aim of reaching an acceptable level of accuracy for the model's given application. The model at present is an ideal vector control model built in Simulink with a discrepancy between 3% and 5% from real world data. The next step is adding losses which can be categorised into 3 main categories: those which occur in the electrical circuit, magnetic circuit, and mechanical and ventilation losses. Furthermore, below 400rpm, the accuracy of the model decreases with speed and is near tangential with the trend line. The cause of this behiaviour could be due to an inaccurate data sheet used to parameterise the model, or limitations in the measurement instrumentation hindering its ability to accurately capture low speed data. The electric machine will then be tested through a predetermined, AVL parameterisation cycle to ascertain whether there is a variation between the true parameters of the machine and those supplied by the manufacturer. If the lack of accuracy at low speeds is due to limitations of the instrumentation, identical tests on a different machine could potentially be conducted at the IAAPS facility.I initially looked into vector control during the summer project, along with the operating principles of synchronous machines and how they were linked. With a background in ICEs and batteries, learning the fundamental characteristics of the type of electric machine I am working with is a necessity. Beginning this project with a laymans knowledge of an electric machine's operation has been a challenge and could lead to delays in the project, however, speaking to the AVL stakeholders to assertain what their objectives for the project will allow me to plan my workflowmore ef

随着汽车行业向零排放机动性的转变，电动机械在汽车行业中变得越来越普遍，因为它们成为道路车辆的主要推进系统。由于超过15%的新车销售是电动的，能够准确地描述电动机器的虚拟特征对于最大化其性能和效率是必不可少的。模型复制暂态行为的能力的一个关键预测因素是用来描述电机特征的参数的准确性。仅仅依靠制造商提供的信息和规范来创建健壮的模型是不切实际的，因为它们通常只包括机器运行所需的信息。这项工作的总体目标是开发一种过程，以自动化电机模型的参数化，以供以后在车辆开发过程中使用。电机模型有许多潜在的用例，以及许多感兴趣的电机架构。在用例和电机架构的每个组合中，适当的电机模型结构预计会有所不同。通常，当需要更深入地了解详细的马达性能时，空间和时间分辨率的水平将会提高。一旦定义了模型结构，就可以定义对该模型进行参数化和验证所需的数据。然后，可以定义生成这些数据所需的实验以及所需的仪器。该项目以电机的模型开发为重点，旨在创建模型和数据之间的端到端工作流，以提高模型的准确性和对新测试单元的适应性。该工作将探索与所有可能的电机拓扑结构兼容的通用电机模型和参数化程序的可能性：磁通开关、感应和同步电机架构。它将专注于实施自主的参数表征过程，并简化电机参数化所需数据收集背后的实验程序。电机的结构千差万别，需要定制的电机模型来模拟其行为。在项目的前6个月中，将创建一个同步电机模型，目的是达到模型给定应用的可接受的精度水平。目前的模型是在SIMULINK中建立的理想的矢量控制模型，与实际数据的误差在3%~5%之间。下一步是增加损失，这些损失可分为3个主要类别：发生在电路、磁路以及机械和通风损失中的损失。此外，在400rpm以下，模型的精度随着速度的增加而降低，并且与趋势线接近切线。出现这种情况的原因可能是用于对模型进行参数化的数据表不准确，或者测量仪器的限制阻碍了其准确捕获低速数据的能力。然后，将通过预定的AVL参数化循环对电机进行测试，以确定机器的真实参数与制造商提供的参数之间是否存在差异。如果低速时的精度不足是由于仪器的限制，那么IAAPS设施可能会在不同的机器上进行相同的测试。我最初在夏季项目中研究了矢量控制，以及同步机器的工作原理和它们是如何连接的。我有冰块和电池的背景，因此有必要了解我使用的这类电机的基本特性。以一个外行的电机操作知识开始这个项目一直是一个挑战，可能会导致项目的延误，然而，与AVL的利益相关者交谈，断言他们的项目目标将使我能够计划我的工作流程更多