Autonomous parameter estimation for 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

Impact Summary

This proposal has been developed from the ground up to guarantee the highest level of impact. The two principal routes towards impact are via the graduates that we train and by the embedding of the research that is undertaken into commercial activity. The impact will have a significant commercial value through addressing skills requirements and providing technical solutions for the automotive industry - a key sector for the UK economy.

The graduates that emerge from our CDT (at least 84 people) will be transformative in two distinct ways. The first is a technical route and the second is cultural.

In a technical role, their deep subject matter expertise across all of the key topics needed as the industry transitions to a more sustainable future. This expertise is made much more accessible and applicable by their broad understanding of the engineering and commercial context in which they work. They will have all of the right competencies to ensure that they can achieve a very significant contribution to technologies and processes within the sector from the start of their careers, an impact that will grow over time. Importantly, this CDT is producing graduates in a highly skilled sector of the economy, leading to jobs that are £50,000 more productive per employee than average (i.e. more GVA). These graduates are in demand, as there are a lack of highly skilled engineers to undertake specialist automotive propulsion research and fill the estimated 5,000 job vacancies in the UK due to these skills shortages. Ultimately, the CDT will create a highly specialised and productive talent pipeline for the UK economy.

The route to impact through cultural change is perhaps of even more significance in the long term. Our cohort will be highly diverse, an outcome driven by our wide catchment in terms of academic background, giving them a 'diversity edge'. The cultural change that is enabled by this powerful cohort will have a profound impact, facilitating a move away from 'business as usual'.

The research outputs of the CDT will have impact in two important fields - the products produced and processes used within the indsutry. The academic team leading and operating this CDT have a long track record of generating impact through the application of their research outputs to industrially relevant problems. This understanding is embodied in the design of our CDT and has already begun in the definition of the training programmes and research themes that will meet the future needs of our industry and international partners. Exchange of people is the surest way to achieve lasting and deep exchange of expertise and ideas. The students will undertake placements at the collaborating companies and will lead to employment of the graduates in partner companies.

The CDT is an integral part of the IAAPS initiative. The IAAPS Business Case highlights the need to develop and train suitably skilled and qualified engineers in order to achieve, over the first five years of IAAPS' operations, an additional £70 million research and innovation expenditure, creating an additional turnover of £800 million for the automotive sector, £221 million in GVA and 1,900 new highly productive jobs.

The CDT is designed to deliver transformational impact for our industrial partners and the automotive sector in general. The impact is wider than this, since the products and services that our partners produce have a fundamental part to play in the way we organise our lives in a modern society. The impact on the developing world is even more profound. The rush to mobility across the developing world, the increasing spending power of a growing global middle class, the move to more urban living and the increasingly urgent threat of climate change combine to make the impact of the work we do directly relevant to more people than ever before. This CDT can help change the world by effecting the change that needs to happen in our industry.