Drivetrain simulation using variational autoencoders

TL;DR

This paper introduces variational autoencoders (VAEs) for predicting vehicle jerk signals from torque demand, enabling efficient drivetrain simulation with limited experimental data.

Contribution

It demonstrates the use of both unconditional and conditional VAEs to synthesize realistic jerk signals across different drivetrain configurations without detailed system modeling.

Findings

VAEs effectively capture characteristics of multiple drivetrain scenarios.

Conditional VAEs generate tailored signals based on specific torque inputs.

The approach reduces reliance on costly real-world experiments.

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.