MRS Meetings and Events

 

MT01.09.01 2024 MRS Spring Meeting

Rapid Discovery of Lightweight Cellular Crashworthy Solids for Battery Electric Vehicles using Artificial Intelligence and Finite Element Modeling

When and Where

Apr 25, 2024
5:00pm - 7:00pm

Flex Hall C, Level 2, Summit

Presenter

Co-Author(s)

Edward Michaud1,2,Dayakar Penumadu2

Volkswagen Group of America1,The University of Tennessee, Knoxville2

Abstract

Edward Michaud1,2,Dayakar Penumadu2

Volkswagen Group of America1,The University of Tennessee, Knoxville2
Battery electric vehicles (BEVs) offer significant environmental benefits over traditional internal combustion engine (ICE) vehicles, including zero tailpipe emissions and improved energy efficiency. Despite this, typical BEV batteries can weigh up to 500kg, substantially heavier than a conventional vehicle's engine and fuel tank. This makes BEVs great candidates for lightweighting, offering improvements in driving range, acceleration, handling, and overall efficiency. At the same time, these batteries must be protected from side-impact even further than a conventional ICE vehicle.<br/>In this study, we aim to leverage computational modeling and artificial intelligence (AI) methods to lightweight the battery's protective structures beyond capabilities achievable by a typical human. Specifically, we focus on the methods necessary to fully train an artificial intelligence architectutre such that models can be rapidly generated and validated hundreds to thousands of times. Ultimately, this research will contribute to the development of more efficient and sustainable BEVs, which can help to mitigate climate change and reduce the environmental impact of transportation while maintaining or impmroving safety standards.

Keywords

strength

Symposium Organizers

Raymundo Arroyave, Texas A&M Univ
Elif Ertekin, University of Illinois at Urbana-Champaign
Rodrigo Freitas, Massachusetts Institute of Technology
Aditi Krishnapriyan, UC Berkeley

Session Chairs

Chris Bartel
Rodrigo Freitas
Sara Kadkhodaei
Wenhao Sun

In this Session

MT01.09.01
Rapid Discovery of Lightweight Cellular Crashworthy Solids for Battery Electric Vehicles using Artificial Intelligence and Finite Element Modeling

MT01.09.02
Chemical Environment Modeling Theory: Revolutionizing Machine Learning Force Field with Flexible Reference Points

MT01.09.03
A Self-Improvable Generative AI Platform for the Discovery of Solid Polymer Electrolytes with High Conductivity

MT01.09.04
Adaptive Loss Weighting for Machine Learning Interatomic Potentials

MT01.09.05
High-Accurate and -Efficient Potential for BCC Iron based on The Physically Informed Artificial Neural Networks

MT01.09.06
Molecular Dynamics Simulation of HF Etching of Amorphous Si3N4 Using Neural Network Potential

MT01.09.07
Deep Potential Model for Analyzing Enhancement of Lithium Dynamics at Ionic Liquid and Perovskite (BaTiO3) Interface

MT01.09.08
Autonomous AI generator for Machine Learning Interatomic Potentials

MT01.09.09
Capturing The Lone Pair Interactions in BaSnF4 Using Machine Learning Potential

MT01.09.10
Benchmarking, Visualization and Hyperparameter Optimization of UF3 to Enhance Molecular Dynamics Simulations

View More »

Publishing Alliance

MRS publishes with Springer Nature