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.

翻译：高精度时间序列振动预测是电动汽车领域的一个重要研究问题。电动汽车在崎岖路面行驶时经常发生振动，即扭转共振。这种由电机与轮胎振动相互作用引起的共振会对车辆传动轴施加过大负荷。然而，现有阻尼技术仅能在传动轴转矩振动幅值达到一定阈值后检测共振，导致检测时传动轴已承受显著载荷。本研究提出一种新方法应对该问题，引入基于Transformer模型的Resoformer，用于预测扭转共振。Resoformer以电机转速时间序列为输入，预测该序列之后指定分位数下传动轴扭转振动的幅值。通过计算实测数据点提取的递归特征与卷积特征之间的注意力机制，Resoformer提升了振动预测精度。为评估模型性能，我们使用包含2600条模拟器生成振动序列的VIBES数据集（电动汽车振动转变预测数据集）。基于VIBES数据集构建的强基线实验表明，Resoformer取得了最先进的结果。本研究回答了"Transformer能否预测振动？"这一问题：尽管传统Transformer架构在预测扭转共振波时表现不佳，但结合循环神经网络与时间卷积网络的Transformer架构可提升长期振动预测精度。