Drivetrain simulation using variational autoencoders

• URL: http://arxiv.org/abs/2501.17653v2
• Date: Sun, 29 Jun 2025 14:26:51 GMT
• Title: Drivetrain simulation using variational autoencoders
• Authors: Pallavi Sharma, Jorge-Humberto Urrea-Quintero, Bogdan Bogdan, Adrian-Dumitru Ciotec, Laura Vasilie, Henning Wessels, Matteo Skull,
• Abstract summary: This work proposes variational autoencoders (VAEs) to predict a vehicle's jerk signals from torque demand.<n>We implement both unconditional and conditional VAEs, trained on experimental data from two variants of a fully electric SUV.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work proposes variational autoencoders (VAEs) to predict a vehicle's jerk signals from torque demand in the context of limited real-world drivetrain datasets. We implement both unconditional and conditional VAEs, trained on experimental data from two variants of a fully electric SUV with differing torque and drivetrain configurations. The VAEs synthesize jerk signals that capture characteristics from multiple drivetrain scenarios by leveraging the learned latent space. A performance comparison with baseline physics-based and hybrid models confirms the effectiveness of the VAEs, without requiring detailed system parametrization. Unconditional VAEs generate realistic jerk signals without prior system knowledge, while conditional VAEs enable the generation of signals tailored to specific torque inputs. This approach reduces the dependence on costly and time-intensive real-world experiments and extensive manual modeling. The results support the integration of generative models such as VAEs into drivetrain simulation pipelines, both for data augmentation and for efficient exploration of complex operational scenarios, with the potential to streamline validation and accelerate vehicle development.

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