Aston University's academics contribute to the development of a novel, real-time wheel alignment fault detection system to allow R L Capital to extend their intelligent HGV Auto Align tyre and wheel offering to cars and vans.

The Company

R L Capital Ltd (RLC) is an automotive technology company that develops intelligent tyre and wheel monitoring systems, reducing carbon and micro-particle vehicle emissions. RLC's strategy is to develop and commercialise a portfolio of novel automotive fault detection systems.

RLC has developed and commercialised two real-time tyre/wheel condition monitoring systems – TACscan (predictive tyre tread wear) and TyreWatch (commercial vehicle tyre pressure monitoring). 

The company has an ongoing technology partnership with McLaren Applied for its TyreWatch system.

The Problem

RLC are now developing AutoAlign, a wheel alignment fault detection system for Heavy Goods Vehicles (HGVs) funded via a Small Business Research Initiative (SBRI) project. The SBRI project accessed academic expertise in micro-electro-mechanical systems (MEMS), dynamics and data analytics to develop AutoAlign. RLC recognised that in order to achieve its strategic goals, it needs to embed this expertise in house, both to extend the functionality of AutoAlign for cars and vans and to deliver the long-term R&D capability for the company. 

RLC has prior experience in fleet monitoring solutions with a team of developers/analysts supporting its software platform. However, it lacks the knowledge in non-linear dynamics, sensors and advanced signal processing and Machine Learning (ML).

The Solution

Through the use of technical enterprise, academics from the College of Engineering and Physical Sciences at Aston, will extend the functionality of AutoAlign HGV into AutoAlign for cars and vans. Joint work by the academics and the company will enable RLC to make a step-change in its technology and accelerate RLC's skills in sensor design, signal processing and ML, which without the KTP could take several years to accumulate.
Aston’s academic team will develop a novel, real-time wheel alignment fault detection system (AutoAlign for Cars & Vans) to deliver accurate fault detection of wheel misalignment, enabling drivers and fleet operators to reduce power consumption and minimise air pollution caused by excess exhaust emissions and tyre wear.

“We are delighted to partner with Aston University on this project which builds on our existing and long-term relationship. Working on sustainability in the Automotive sector using IoT sensors, Big Data and Machine Learning is stimulating, rewarding and is at the forefront of real-world technologies.” Mark Longden, RL Capital’s CEO.

Further environmental benefits of the project include: 

  • A recent survey by Protyre of 376,000 cars showed that 46% of cars had misaligned wheels – with AutoAlign this could be significantly reduced, contributing to the Government’s Clean Air Strategy to decrease CO2 and non-exhaust particulate emissions.
  • AutoAlign will enable drivers to cut overall annual release of tyre-related particulates by up to 5% and remove up to 25,000 tons of microplastics from the environment in the EU.
  • AutoAlign will also reduce exhaust emissions from lower fuel consumption and cut 540 million tons of annual worldwide CO2 emissions from manufacture and subsequent disposal of up to 30 million car tyres (CRR).
  • AutoAlign will encourage operators to proactively correct alignment and it is estimated that within five years an additional 4 million alignment resets will be performed per year in the UK, generating up to 600 UK automotive jobs.

The project outcomes will be integrated into Aston's curriculum via teaching case studies developing well equipped graduates.

Team Aston

The project brings together a strong team of academics addressing the challenge of achieving the company’s long-term aims. The team includes Dr Yu Jia, a Senior Lecturer in Mechanical Engineering at Aston University who leads the Smart Microsystem Research Group. Her expertise lies in signal processing and conditioning of data. 

Dr Jia will work alongside Dr Maria Chli, whose core research is modelling and optimisation of  complex systems through coupling techniques from the fields of multi-agent systems and ML.

Academic support will be provided by Dr George Vogiatzis, who has extensive experience in applying constrained optimisation techniques, including Mixed Integer Programming in a variety of complex geometric problems. He has extensive experience of ML techniques including deep learning neural networks.

“The successful outcome of this project will eventually result in a reduction in micro-particulate air pollution from the wheels of road cars, thus resulting in a cleaner environment, improvement to public health as well as reducing the vehicle on-cost from the reduced wear and tear.” Dr Yu Jia, Senior Lecturer in Mechanical Engineering, Aston University.

Next steps

The KTP is expected to complete in 2022 – look out for more updates by following us on Twitter and checking our webpage

For more information about Knowledge Transfer Partnerships or an informal chat, email ktp@aston.ac.uk or call 0121 204 4242.