  Highly accurate time-series vibration prediction is an important research
issue for electric vehicles (EVs). EVs often experience vibrations when driving
on rough terrains, known as torsional resonance. This resonance, caused by the
interaction between motor and tire vibrations, puts excessive loads on the
vehicle's drive shaft. However, current damping technologies only detect
resonance after the vibration amplitude of the drive shaft torque reaches a
certain threshold, leading to significant loads on the shaft at the time of
detection. In this study, we propose a novel approach to address this issue by
introducing Resoformer, a transformer-based model for predicting torsional
resonance. Resoformer utilizes time-series of the motor rotation speed as input
and predicts the amplitude of torsional vibration at a specified quantile
occurring in the shaft after the input series. By calculating the attention
between recursive and convolutional features extracted from the measured data
points, Resoformer improves the accuracy of vibration forecasting. To evaluate
the model, we use a vibration dataset called VIBES (Dataset for Forecasting
Vibration Transition in EVs), consisting of 2,600 simulator-generated vibration
sequences. Our experiments, conducted on strong baselines built on the VIBES
dataset, demonstrate that Resoformer achieves state-of-the-art results. In
conclusion, our study answers the question "Can Transformers Forecast
Vibrations?" While traditional transformer architectures show low performance
in forecasting torsional resonance waves, our findings indicate that combining
recurrent neural network and temporal convolutional network using the
transformer architecture improves the accuracy of long-term vibration
forecasting.
