Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance

TL;DR

This paper introduces a probabilistic surrogate model using Gaussian Process Regression to efficiently predict crash performance of electric vehicle battery enclosures, enabling faster and more reliable design optimization.

Contribution

The study develops a novel surrogate modeling approach that combines high-throughput simulations with Gaussian Process Regression for accurate crash performance prediction.

Findings

Predictive accuracy within 8.08% error for all outputs

Effective uncertainty quantification through Monte Carlo analysis

Robust model capturing complex relationships in design parameters

Abstract

This paper presents a probabilistic surrogate model for the accelerated design of electric vehicle battery enclosures with a focus on crash performance. The study integrates high-throughput finite element simulations and Gaussian Process Regression to develop a surrogate model that predicts crash parameters with high accuracy while providing uncertainty estimates. The model was trained using data generated from thermoforming and crash simulations over a range of material and process parameters. Validation against new simulation data demonstrated the model's predictive accuracy with mean absolute percentage errors within 8.08% for all output variables. Additionally, a Monte Carlo uncertainty propagation study revealed the impact of input variability on outputs. The results highlight the efficacy of the Gaussian Process Regression model in capturing complex relationships within the dataset, offering a robust and efficient tool for the design optimization of composite battery enclosures.