Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909280861421568 |
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| author | Shaikh, Shadab Anwar Cherukuri, Harish Balusu, Kranthi Devanathan, Ram Soulami, Ayoub |
| author_facet | Shaikh, Shadab Anwar Cherukuri, Harish Balusu, Kranthi Devanathan, Ram Soulami, Ayoub |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03450 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance Shaikh, Shadab Anwar Cherukuri, Harish Balusu, Kranthi Devanathan, Ram Soulami, Ayoub Machine Learning Computational Engineering, Finance, and Science 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. |
| title | Probabilistic Surrogate Model for Accelerating the Design of Electric Vehicle Battery Enclosures for Crash Performance |
| topic | Machine Learning Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2408.03450 |