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.

翻译：本研究提出采用变分自编码器（VAEs），在真实世界传动系统数据集受限的情况下，根据扭矩需求预测车辆的加加速度信号。我们实现了无条件与条件变分自编码器，并利用两款全电动SUV（其扭矩配置与传动系统参数不同）的实验数据进行训练。通过利用学习到的潜在空间，VAEs能够合成捕捉多种传动系统场景特征的加加速度信号。与基于物理模型及混合模型的基准方法进行性能对比，验证了VAEs的有效性，且无需详细的系统参数化。无条件VAE无需先验系统知识即可生成真实的加加速度信号，而条件VAE则能生成针对特定扭矩输入的定制化信号。该方法降低了对昂贵且耗时的实车实验及大量手动建模的依赖。研究结果支持将VAEs这类生成模型集成至传动系统仿真流程中，可用于数据增强与复杂运行工况的高效探索，有望简化验证流程并加速车辆开发进程。