Presentation
Unlocking the Future of Battery Innovation: Harnessing Advanced AI in Materials Science, Manufacturing, and Safety
DescriptionIn 2023, global electric vehicle (EV) sales reached a record 14 million and are projected to rise to 17 million by the end of 2024, with one in five new cars sold worldwide expected to be an EV. This shift is essential, as transportation accounts for about 20% of global CO2 emissions, and innovations like Urban Air Mobility (UAM) will enhance electric transport's role in combating climate change. However, advancing electric transportation hinges on overcoming two major battery-related challenges.
First, the exploration of battery materials has been limited; the industry has studied only around 1,000 unique small molecules over the past 30 years, despite the existence of approximately 10^11 potential candidates. To identify ideal electrolyte materials, we must leverage advanced in-silico chemistry techniques like DFT calculations and molecular dynamics simulations.
Second, ensuring manufacturing quality and safety at scale is a daunting task, as gigafactories produce up to 1 million cells daily but often conduct thorough quality checks on only 1% of them. This may allow defective cells to go unnoticed, compromising safety. Managing trillions of cycles of lifecycle data requires advanced GPUs like the H100 for computation-intensive tasks, such as real-time CT rendering, ensuring high standards of quality assurance in EV battery production. Addressing these challenges is crucial for the successful scaling of electric transportation and achieving a sustainable future.
First, the exploration of battery materials has been limited; the industry has studied only around 1,000 unique small molecules over the past 30 years, despite the existence of approximately 10^11 potential candidates. To identify ideal electrolyte materials, we must leverage advanced in-silico chemistry techniques like DFT calculations and molecular dynamics simulations.
Second, ensuring manufacturing quality and safety at scale is a daunting task, as gigafactories produce up to 1 million cells daily but often conduct thorough quality checks on only 1% of them. This may allow defective cells to go unnoticed, compromising safety. Managing trillions of cycles of lifecycle data requires advanced GPUs like the H100 for computation-intensive tasks, such as real-time CT rendering, ensuring high standards of quality assurance in EV battery production. Addressing these challenges is crucial for the successful scaling of electric transportation and achieving a sustainable future.
Event Type
Exhibits
Flash Session
TimeThursday, 21 November 20242pm - 2:15pm EST
LocationBooth 4155
TP
XO/EX
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