Plenary Speakers

Dr. Martin Webster

Senior Research Associate, Lubricants Technology  
ExxonMobil Research and Engineering

Lubrication and Tribology Trends and Challenges in Electric Vehicles

Recent studies such as STLE's Emerging Trends reports confirm the adoption of electric vehicle (EV) technology is increasing and now represents one of the fastest growing passenger vehicle segments. This will have a significant impact on future vehicle fluid requirements, component design and overall vehicle architecture. While at first glance the use of familiar mechanical components might suggest otherwise, EVs will pose some interesting tribological and fluid technology challenges.

This presentation will cover the key drivers behind the vehicle electrification revolution. It will explain why electrification is an increasingly attractive route toward meeting current and projected future emission goals. A brief review of the major components that make up a battery electric vehicle (BEV) will be presented. The operating characteristics of electric motors impacts the design of the gearbox and driveline components. In turn, this impacts future lubrication requirements and introduces some significant opportunities for innovation. In particular, the need to balance supporting high torque loads at low speed versus lubrication at very high motor speeds represents a difficult compromise between antagonistic lubrication requirements.

Thermal management and cooling has emerged as one of the key fluid requirements in EVs. Multiple components such as batteries, motors and electronics have unique thermal management requirements. Currently there is a diversity of approaches being used to achieve effective thermal management, placing different demands on the fluids. One option of combining cooling and lubrication into a single system using the same fluid offers advantageous simplifications. However, the resulting fluid would need to meet a very demanding series of new performance requirements.

Finally, we will highlight the need for the pro-active participation of the tribology community in developing this rapidly developing technology. Previous experience in other areas has shown that tribology can play a key enabling role in early identification of critical challenges and finding appropriate solutions. STLE is responding to this challenge by providing featured content in its publications and a forum for idea exchange.


Martin holds BSc and MSc degrees in Aeronautical Engineering and a PhD in Tribology all from Imperial College London.  In 1986 he received the Tribology Bronze medal from the I. Mech E. for his work on rough surface contact mechanics.  Following spells as a Post-Doctoral intern at Shell Research and an engineering position at Taylor Woodrow’s Wind Energy Group he moved to the USA in 1989 to join Mobil’s Central Research Laboratory.  Following a 30 year career in fundamental research and product development he is currently engaged in applied research within ExxonMobil’s Lubricants Technology Department located in Clinton, NJ.  Martin has published papers, patents and text books on contact mechanics, EHL, traction, gear and bearing fatigue, micropitting, gear oil development, DLC coatings, mixed lubrication, hydrodynamics, new lubricant components, novel lubrication mechanisms and test methods.  Martin is a co-author of the 3rd edition of ExxonMobil’s Lubrication Fundamentals textbook that was published in 2016.  Over the last 30 years he has also been highly active within the Society of Tribologists and Lubrication Engineers (STLE).  In 2006 he was elected to join the STLE Board of Directors culminating with him joining the Executive Committee.  He served as the STLE President 2015-16.  In 2019 he was elected a Fellow of the STLE.

Dr. Marius Stan

Senior Scientist and Program Lead, Intelligent Materials Design
Argonne National Laboratory

"Artificial Intelligence for Material and Process Design"

Modeling properties and evolution of complex systems requires a comprehensive evaluation of uncertainty and model quality using experimental, theoretical and computational methods that operate at vastly different length and time scales. The continuous increase of the volume and rate of data generation makes human analysis more difficult, if not impossible. Fortunately, recent advances in artificial intelligence (AI) have significantly improved R&D methodologies by emphasizing the role of the human-machine partnership. We discuss the development of “intelligent software” that includes elements of AI such as Machine Learning, Active Learning, Computer Vision and Augmented Reality, coupled with Reduce-Order Modeling and Bayesian analysis. We illustrate the value of the approach using examples of machine learning modeling of material properties and real-time optimization of manufacturing processes. 
Dr. Marius Stan is the Intelligent Materials Design Lead in the Argonne National Laboratory’s Applied Materials division. Stan is a computational physicist and chemist interested in complexity, non-equilibrium thermodynamics, heterogeneity, and materials design for energy and electronics applications. He uses artificial intelligence, machine learning, and multi-scale computer simulations to understand and predict properties and evolution of complex physical systems.

Stan came to Argonne and the University of Chicago in 2010, from Los Alamos National Laboratory. He is a Senior Fellow at the University of Chicago’s Computation Institute (CI) and a senior Fellow of the Northwestern-Argonne Institute for Science and Engineering (NAISE).

The goal of Stan’s research is to discover or design materials, structures, and device architectures for energy applications, such as nuclear energy and energy storage, and for the new generation computers. To that end, he develops theory-based (as opposite to empirical) mathematical models of thermodynamic and chemical properties of imperfect materials. The imperfection comes from defects or deviations from stoichiometry (e.g., in battery electrodes), from irradiation (e.g. in nuclear fuels), or doping (e.g. computer memory devices). Then Stan uses the models in computer simulations of coupled heat and chemical transport, micro(nano)-structure evolution, phase-stability, and phase transformations. To analyze large and complex experimental and computational data sets, Stan uses Bayesian analysis and machine learning methods based on regression and evolutionary (genetic) algorithms that can produce robust data screening and sampling. In parallel, Stan designs experiments to validate the models and simulations. Click here to learn more.

Dr. Karin A. Dahmen

Professor, Department of Physics
University of Illinois at Urbana-Champaign

"Universal Avalanche Statistics Across 16 Decades in Length: Connecting External to Internal Friction from Nanocrystals to Earthquakes to Stars"

Slowly-compressed nano-crystals, bulk metallic glasses, rocks, granular materials, and the earth all deform via intermittent slips or “quakes.” We find that although these systems span 12 decades in length scale, they all show the same scaling behavior for their slip-size distributions and other statistical properties. Remarkably, the size distributions follow the same power law multiplied with a stress-dependent cutoff, indicating an underlying non-equilibrium phase transition. A simple mean field model for avalanches of slipping weak spots explains the agreement across scales. It predicts the observed slip-size distributions and the observed stress-dependent cutoff function. The analysis draws on tools from statistical physics and the renormalization group. The results enable extrapolations from one scale to another, and from one force to another, across different materials and structures from nanocrystals to earthquakes. Connections to friction and recent observations on stars will also be discussed, extending the range of scales to 16 decades in length.
Dr. Karin Dahmen received her Vordiplom in physics from the Universität Bonn, Germany, in 1989, and her doctorate in physics from Cornell University in 1995. Before joining the faculty at the University of Illinois in 1999, she was a Junior Fellow at Harvard University. She has wide-ranging interests in soft condensed matter physics, including non-equilibrium dynamical systems, hysteresis, avalanches, earthquakes, population biology, and disorder-induced critical behavior.