For the better part of a century, General Motors (GM) was the largest automaker in the world. Today, amid a paradigm shift toward smarter electrified vehicles, America’s leading automaker is scrambling to respond to the news – and to do so, it’s leveraging deep partnerships with labs. American nationals, from their particle accelerators to their supercomputers. At a meeting of the Department of Energy’s Advanced Scientific Computing Advisory Committee (ASCAC) last week, Paul Krajewski – director of vehicle systems research for GM’s research and development center – pointed out the depth and value of GM’s work with national labs to leverage HPC and advance vehicle technology.
Many national partner laboratories covered by HPCwire only find the need for national laboratories when they reach the limits of their own computing resources. But GM, Krajewski explained, has close relationships with National Laboratories that permeate many facets of its research and development processes — not just through their high-performance computing equipment, but also experimental equipment, research and software expertise.
Krajewski pointed out that national lab collaborations with GM have taken many forms: stand-alone projects, contracts, partnerships, mutual participation in broader DOE contracts, use of facilities, discussions, paper publications. Throughout these collaborations, he said, GM has worked to leverage the “excellent researchers” and “unique capabilities” of national labs, with an emphasis on data collection.
“One … opportunity that I think is really important for this collaboration is combined access to data,” he said. “Computing doesn’t give us the right answers if we don’t have the data – if we don’t have the ground truth and we don’t have the experimental results.”
Krajewski gave a high-level overview of where GM invests its money in research: “Battery technology, range, fuel cell, advanced manufacturing,” with “almost half” of the funding going to battery technology. batteries and electrification.
“We are committed to an electric future,” Krajewski said. GM, he explained, had partnered with National Laboratories through a consortium called Battery500. This consortium – which includes four national laboratories (led by the Pacific Northwest National Lab) and five universities – aims to develop high-energy rechargeable lithium-metal batteries for electric vehicles. (Lithium-metal batteries are distinct from lithium-ion batteries, offering significantly higher energy density but suffering from stability issues.)
Through Battery500, GM worked with national laboratories to develop new processes and materials for lithium-metal batteries, strongly informing subsequent simulations and model development. “It’s really important to generate the experimental data that we need to then create the models,” Krajewski said. “There is also work on multi-scale modeling of lithium-ion batteries and eventually this will go beyond lithium-metal batteries, and this is where computational capability is extremely important. If you want to be able to do these multi-physics, multi-scale models that are very, very computationally intensive, you have to have that power that resides in the national labs.
But GM isn’t putting all of its eggs in the electric battery basket – much of GM’s other research focuses on fuel cell technology, which would use hydrogen fuel cells to power electric motors, with only water and heat as by-products. Krajewski said there is “an enormous amount of work underway” with the National Renewable Energy Laboratory (NREL) on fuel cells, looking at both experimental capability and computer modeling. For example, GM helped fund the hydrogen fuel cell filling simulation project “H2FiLLS”, which made good use of NREL’s Intel-powered Eagle supercomputer (8 petaflops peak) to run computational dynamics models. hydrogen storage tank fluids, blending simulation results with experimental testing to further refine the simulation.
Similarly, the laboratories and GM have worked together on the thermal management of electric vehicles. “Whether it’s the thermal management of the batteries themselves or the electric motors, that’s becoming a very big challenge,” Krajewski said, “and so there’s work going on on the analysis of the management thermal with Argonne. [National Laboratory] about engines and how [we can] optimize pushing these motors to their highest performance… without creating overheating issues and how to deal with the thermal stresses we end up building up in these motors when we push them to their limits. This work is powered by an allocation of 5 million core hours on Argonne’s Intel-powered Theta system (6.9 petaflops Linpack).
Manufacturing and lightweighting are also top priorities for GM. “When you look at the data we have on manufacturing capacity going forward, and when you think about a transition to chip production, mining, critical materials – all of those things are going to require manufacturing models. so we can be very effective with that,” Krajewski said.
On the lightweight front, GM has worked with Argonne National Laboratory’s Advanced Photon Source (APS) particle accelerator to work on characterizing so-called “Gen 3” steel, which aims to be stronger (and therefore requires less material for sufficient performance and safety in vehicles). These experimental results from APS were then combined with finite element optimization on NREL’s (now decommissioned) Peregrine cluster. GM has also partnered with Oak Ridge National Laboratory (ORNL) to develop a new aluminum alloy (DuAlumin 3D) through a combination of computational and experimental analysis.
“How can I use these materials once I’ve developed this?” Krajewski continued. “I now have a suite of materials that I could apply; I have the performance requirements – in this case, … a side body structure with different parts on it; I could run finite element optimization using NREL’s cluster to optimize what material, what gauge, where I want to use it, and really figure out the best solution to apply those materials I’ve also designed leveraging those techniques. ”
And then, even later in the design process, GM is “aiming for 100% virtual validation by 2025” to eliminate unnecessary dies, tools and prototype parts and to speed up vehicle development. “When we have these material models, now [you] can do things like predict performance, predict formability – can I create the shapes I want to create? – static and dynamic principles, crash performance,” he continued.
“These collaborations are key to driving technology development,” Krajewski concluded. “There are many models for this collaboration, and combining the ability to experiment with this computational ability is key to these collaborations and really pushing the technology forward.”
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