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.
Allu, Srikanth, et al. "Accelerating Battery Simulations by Using High Performance Computing and Opportunities with Machine Learning." , vol. 62, Mar. 2023. https://doi.org/10.1007/978-3-031-17607-4_7
Allu, Srikanth, Fattebert, Jean-Luc, Wang, Hsin, Simunovic, Srdjan, Pannala, Sreekanth, & Turner, John (2023). Accelerating Battery Simulations by Using High Performance Computing and Opportunities with Machine Learning. , 62. https://doi.org/10.1007/978-3-031-17607-4_7
Allu, Srikanth, Fattebert, Jean-Luc, Wang, Hsin, et al., "Accelerating Battery Simulations by Using High Performance Computing and Opportunities with Machine Learning," 62 (2023), https://doi.org/10.1007/978-3-031-17607-4_7
@book{osti_1965276,
author = {Allu, Srikanth and Fattebert, Jean-Luc and Wang, Hsin and Simunovic, Srdjan and Pannala, Sreekanth and Turner, John},
title = {Accelerating Battery Simulations by Using High Performance Computing and Opportunities with Machine Learning},
annote = {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.},
doi = {10.1007/978-3-031-17607-4\{_}7},
url = {https://www.osti.gov/biblio/1965276},
issn = {ISSN 0076-9924},
volume = {62},
place = {United States},
publisher = {Springer,Cham,Switzerland},
year = {2023},
month = {03}}