Physics-informed Artificial Intelligence for Cutting Brake Emissions from Electric Vehicles

This project aims at developing a new braking control system for electric vehicles using artificial intelligence (AI) for a significant reduction of (i) brake particle emissions and of (ii) noise emissions. Both emissions are major sources of environmental pollution and health hazards in urban areas: Today there is consensus that today's vehicle friction brakes emit about the same amount of dust as present-day combustion engines. With electric drives, the friction brake (jointly with the tyres) will thus become the primary emitter of highly toxic dust from vehicles. A similar argument applies to noise: with most of the noise sources of combustion engines gone, the relative sliding of brake components will become even more audible and harmful than it is already today. Due to the complexity of the physics and chemistry involved in frictional braking, up to the present day, and despite decade long intense efforts, it has not been possible to generate satisfactory models, or a 'virtual twin', starting from fundamental theory and basic principles. Recent disruptive progress through AI methods suggests that a combination of physics and data-processing based methods is the way to move forward to applicable modelling, simulation and control for a greener and healthier mobility. With the paradigm change towards electric vehicles ahead, there now is the pressing need for obtaining a control of the future friction brakes with respect to cutting down emissions of particles and noise by intelligent use of braking. Having two means of braking (electric and mechanic) opens up a way of optimizing their combination. Different AI methods will be integrated: to enrich the physical models with a virtual sensor, to develop emission descriptors and to continuously improve the control strategy by learning with use. Models for predicting the load variables expected during the next braking event will determine the risk of a sharp increase in emissions in order to avoid critical operating points by using data-based control (learning by reinforcement) of electrical and mechanical brakes. The operation of the system will be demonstrated on a laboratory prototype. Hamburg University of Technology has a long-standing expertise on data science and computer modelling of noise emissions from friction brakes and is one of the leading teams in the field in Germany and world-wide. University of Lille, which has a long experience of friction interfaces and wear problems, is the leading European institution working on the tribology of friction brakes. Volkswagen AG and AUDI AG are Germany’s leading car manufacturers and allow access to real-world commercial brake testing and field data. Hitachi Automotive Systems is a world-leading friction brake manufacturer. All partners in total provide the complete knowledge, capability and excellence to develop this new AI based braking control strategy that will help industrial partners to develop new digital products, while research institutions will define future AI requirements for mechanical systems. In this way, the project represents the core of future interdisciplinary collaborations and technological developments with the aim of achieving environmentally friendly transport. The project is thus strengthening the European mobility industry, and at the same time pushing AI research for integration into mechanical systems.