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Ford receives DOE HPC4Mfg award to optimize process for electrode drying in battery manufacturing

The US Department of Energy (DOE) announced a $1.8-million investment from the High-Performance Computing for Manufacturing (HPC4Mfg) program for six teams which will tap into the US National Laboratories’ high-performance computing (HPC) resources to help manufacturers streamline their processes, increase their productivity, and lower their carbon footprint.

High-performance computing enables researchers to perform virtual experiments by applying advanced modeling, simulation, and data analysis to manufacturing processes. Running these experiments on supercomputers instead of in the real world allows manufacturers to test new ideas while saving energy, time, and resources.

One of the six awards goes to Ford, to support the development of a high-fidelity CFD model for solvent evaporation and transport in porous structures during battery electrode drying.

An efficient battery manufacturing process is the key to the mass production of Electric Vehicles (EV), in which drying is one of the most energy-intensive steps significantly influencing the battery performance.

An accurate 3D CFD model for drying is essential for predicting the drying mechanism and optimizing its parameters. By optimizing the drying process, it is possible to reduce energy consumption during battery manufacturing, minimize binder loading and maximize active material loading to achieve superior electrochemical performances and facilitate faster public adoption of EVs.

Ford is seeking to leverage the expertise at Sandia National Laboratories to develop a high-fidelity model for solvent evaporation and transport during drying in a porous electrode structure. Potential savings from 10% improvement and speedup on the drying process is 300GWh /year of electrical energy and 10 million tonnes/year of CO2 emission at Ford and its supply base and five times the projected savings nationwide.

The other selected projects are:

  • Danieli USA – Steelmaking currently accounts for 8% of global carbon emissions. Danieli USA will collaborate with National Renewable Energy Laboratory to develop computational simulation models of the melting processes of direct reduced iron (DRI) and H2DRI for industrial use, accelerating the adoption of low-carbon steelmaking. This could help reduce CO2 emissions by up to 32 million tons per year.

  • Allegheny Technologies Inc. – Manufacturing of near-net shape mill-products used in aerospace, automotive and other industries has the potential to significantly reduce both energy use and associated CO2 emissions. Allegheny Technologies Incorporated and Lawrence Livermore National Laboratory will collaborate to produce HPC-enabled digital twin manufacturing for sustainable metalworking that could reduce material waste from the manufacturing process by 50% and CO2 emissions by 564 tons per year.

  • Siemens – Composite Phase change materials (C-PCM) play a critical role in energy and storage industrial applications to drive efficiency improvements, thermal energy management, and carbon emissions reductions. Siemens and Oak Ridge National Laboratory will use HPC to enable high-resolution modeling of the C-PCM microstructure to design better materials for waste heat capture.

  • Solar Turbines Inc. – Solar Turbines Incorporated will use Oak Ridge National Laboratory's HPC expertise to use crystal plasticity finite element (CPFE) modeling to quantify the factors that drive additive manufacturing surface fatigue behavior. This could reduce CO2 emissions by up to 376 million tons per year.

  • M2X Energy Inc. – M2X Energy Inc. works to mitigate methane and carbon dioxide emissions by replacing gas flares with systems that manufacture economically viable, low-carbon chemical products. With the HPC capabilities of Argonne National Laboratory, M2X Energy Inc. will optimize engine design for methane to syngas reformation resulting in a reduction of greenhouse gas emissions and energy consumption from the global upstream oil and gas sector by 43 million metric tons per year.


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