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Accelerating Battery Simulations by Using High Performance Computing and Opportunities with Machine Learning

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Computer Aided Engineering of Batteries

Part of the book series: Modern Aspects of Electrochemistry ((MAOE,volume 62))

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Abstract

The US Department of Energy (DOE) estimates that the battery pack cost of $60 per kilowatt-hour while increasing the driving range to over 300 miles and vehicle charging under 15 min or less would enable mass penetration of electric vehicles in the USA by 2030. These projections are based on the currently available high-density cell chemistry combined with a system level design and optimized electrodes. Electrodes for current state-of-the-art lithium-ion cell technology are fabricated from electrode slurry comprising of active material, polymeric binder, and conductive diluent such as carbon black that are coated on metal current collectors such as copper and aluminum. Given these challenging requirements for development of electrical energy storage devices for future transportation needs, a predictive simulation capability which can accelerate design by considering performance and safety implications of different geometry, materials, and chemistry choices is required. In this chapter, we discuss our state-of-the-art three-dimensional modeling framework, providing examples on battery performance and safety simulations. We also present approaches to use machine learning in the context of large datasets that are becoming available.

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Notes

  1. 1.

    Significant portion of the derivations presented in this section were first published in [2] and are reproduced with permissions from Elsevier.

  2. 2.

    The results presented in this section were first published in [46] and are reproduced with permissions from Elsevier.

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Acknowledgements

This work is supported by the U.S. Department of Energy’s Vehicle Technologies Office at Oak Ridge National Laboratory under Contract No. DE-AC05-00OR22725 with UT-Battelle, LLC. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Authors and Affiliations

  1. Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Srikanth Allu, Jean-Luc Fattebert, Hsin Wang, Srdjan Simunovic & John Turner

  2. Corporate Technology and Innovation, SABIC, Sugar Land, TX, USA

    Sreekanth Pannala

Authors
  1. Srikanth Allu
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  2. Jean-Luc Fattebert
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  3. Hsin Wang
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  4. Srdjan Simunovic
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  5. Sreekanth Pannala
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  6. John Turner
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Corresponding author

Correspondence to Srikanth Allu .

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Editors and Affiliations

  1. National Renewable Energy Laboratory, Golden, CO, USA

    Shriram Santhanagopalan

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Allu, S., Fattebert, JL., Wang, H., Simunovic, S., Pannala, S., Turner, J. (2023). Accelerating Battery Simulations by Using High Performance Computing and Opportunities with Machine Learning. In: Santhanagopalan, S. (eds) Computer Aided Engineering of Batteries. Modern Aspects of Electrochemistry, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-031-17607-4_7

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  • DOI: https://doi.org/10.1007/978-3-031-17607-4_7

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