Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance
- URL: http://arxiv.org/abs/2408.03450v1
- Date: Tue, 6 Aug 2024 21:03:16 GMT
- Title: Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance
- Authors: Shadab Anwar Shaikh, Harish Cherukuri, Kranthi Balusu, Ram Devanathan, Ayoub Soulami,
- Abstract summary: This paper presents a probabilistic surrogate model for the accelerated design of electric vehicle battery enclosures with a focus on crash performance.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction [1.6874375111244329]
This study proposes an Adaptive Ensemble Sparse Identification (AESI) framework that enhances the accuracy of reduced-order li-ion battery models.<n>The approach integrates an Extended Single Particle Model (ESPM) with an evolutionary ensemble sparse learning strategy to construct a robust hybrid model.<n> Evaluation across diverse operating conditions shows that the hybrid model (ESPM + AESI) improves the voltage prediction accuracy, achieving mean squared error reductions of up to 46% on unseen data.
arXiv Detail & Related papers (2025-07-01T01:00:07Z) - Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems [39.979007027634196]
The effective contact area plays a critical role in multi-physics phenomena such as wear, sealing, and thermal or electrical conduction.<n>This study proposes a surrogate modeling framework for predicting the effective contact area using fast-to-evaluate data-driven techniques.
arXiv Detail & Related papers (2025-04-24T08:15:46Z) - On conditional diffusion models for PDE simulations [53.01911265639582]
We study score-based diffusion models for forecasting and assimilation of sparse observations.
We propose an autoregressive sampling approach that significantly improves performance in forecasting.
We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
arXiv Detail & Related papers (2024-10-21T18:31:04Z) - Event-Based Simulation of Stochastic Memristive Devices for Neuromorphic Computing [41.66366715982197]
We build a general model of memristors suitable for the simulation of event-based systems.
We extend an existing general model of memristors to an event-driven setting.
We demonstrate an approach for fitting the parameters of the event-based model to the drift model.
arXiv Detail & Related papers (2024-06-14T13:17:19Z) - Addressing Misspecification in Simulation-based Inference through Data-driven Calibration [43.811367860375825]
Recent work has demonstrated that model misspecification can harm simulation-based inference's reliability.
This work introduces robust posterior estimation (ROPE), a framework that overcomes model misspecification with a small real-world calibration set of ground truth parameter measurements.
arXiv Detail & Related papers (2024-05-14T16:04:39Z) - Ensemble models outperform single model uncertainties and predictions
for operator-learning of hypersonic flows [43.148818844265236]
Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situations that have not been seen before.
High-fidelity data is itself in limited quantity to validate all outputs of the SciML model in unexplored input space.
We extend a DeepONet using three different uncertainty mechanisms: mean-variance estimation, evidential uncertainty, and ensembling.
arXiv Detail & Related papers (2023-10-31T18:07:29Z) - Finding the Perfect Fit: Applying Regression Models to ClimateBench v1.0 [0.0]
ClimateBench is a benchmarking dataset for evaluating the performance of machine learning emulators designed for climate data.
This study focuses on evaluating non-linear regression models using the aforementioned dataset.
arXiv Detail & Related papers (2023-08-23T01:08:01Z) - Learning battery model parameter dynamics from data with recursive
Gaussian process regression [0.0]
We propose a hybrid approach combining data- and model-driven techniques for battery health estimation.
Specifically, we demonstrate a Bayesian data-driven method, Gaussian process regression, to estimate model parameters as functions of states, operating conditions, and lifetime.
Results show the efficacy of the method, on both simulated and measured data, including accurate estimates and forecasts of battery capacity and internal resistance.
arXiv Detail & Related papers (2023-04-26T16:40:34Z) - Adaptive LASSO estimation for functional hidden dynamic geostatistical
model [69.10717733870575]
We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hiddenstatistical models (f-HD)
The algorithm is based on iterative optimisation and uses an adaptive least absolute shrinkage and selector operator (GMSOLAS) penalty function, wherein the weights are obtained by the unpenalised f-HD maximum-likelihood estimators.
arXiv Detail & Related papers (2022-08-10T19:17:45Z) - Robust Output Analysis with Monte-Carlo Methodology [0.0]
In predictive modeling with simulation or machine learning, it is critical to accurately assess the quality of estimated values.
We propose a unified output analysis framework for simulation and machine learning outputs through the lens of Monte Carlo sampling.
arXiv Detail & Related papers (2022-07-27T16:21:59Z) - Prediction of liquid fuel properties using machine learning models with
Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels.
Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach.
The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
arXiv Detail & Related papers (2021-10-18T14:43:50Z) - Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently.
There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance.
We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
arXiv Detail & Related papers (2021-06-17T00:49:55Z) - Nonintrusive Uncertainty Quantification for automotive crash problems
with VPS/Pamcrash [0.0]
surrogate models (metamodels) allow drastically reducing the computational cost of Monte Carlo process.
kernel Principal Component Analysis (kPCA) is found to be effective to simplify the model outcome description.
A benchmark crash test is used to show the efficiency of combining metamodels with kPCA.
arXiv Detail & Related papers (2021-02-15T16:59:39Z) - Model-based Policy Optimization with Unsupervised Model Adaptation [37.09948645461043]
We investigate how to bridge the gap between real and simulated data due to inaccurate model estimation for better policy optimization.
We propose a novel model-based reinforcement learning framework AMPO, which introduces unsupervised model adaptation.
Our approach achieves state-of-the-art performance in terms of sample efficiency on a range of continuous control benchmark tasks.
arXiv Detail & Related papers (2020-10-19T14:19:42Z) - DISCO: Double Likelihood-free Inference Stochastic Control [29.84276469617019]
We propose to leverage the power of modern simulators and recent techniques in Bayesian statistics for likelihood-free inference.
The posterior distribution over simulation parameters is propagated through a potentially non-analytical model of the system.
Experiments show that the controller proposed attained superior performance and robustness on classical control and robotics tasks.
arXiv Detail & Related papers (2020-02-18T05:29:40Z) - Stable Prediction with Model Misspecification and Agnostic Distribution
Shift [41.26323389341987]
In machine learning algorithms, two main assumptions are required to guarantee performance.
One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified.
Under model misspecification, distribution shift between training and test data leads to inaccuracy of parameter estimation and instability of prediction across unknown test data.
We propose a novel Decorrelated Weighting Regression (DWR) algorithm which jointly optimize a variable decorrelation regularizer and a weighted regression model.
arXiv Detail & Related papers (2020-01-31T08:56:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.