Abstract: The height of center of gravity z is one of the key parameters to evaluate the performance and safety of vehicle. When measuring the vehicle’s height (z) using the tilt-table method, under the unlocked condition of the vehicle suspension, the lateral position (y) and height (z) of changed in a coupled manner due to suspension deformation in the process of tilting, making independent measurement difficult. Taking the arithmetic mean value of height (z) of the vehicle when it was titled 6°~12° to the left and right as the final measurement result of height (z) of the vehicle at 0°, resulted in a significant error. To solve this problem, a combined model of convolutional neural network (CNN) and long short-term memory (LSTM) networks incorporating a physical hybrid loss function, namely the CNN-LSTM model, was proposed. The results indicate that compared to Transformer, CNN and LSTM models, the CNN-LSTM model features faster convergence speed and higher prediction accuracy. height (z) at 0° predicted by the CNN-LSTM model is more accurate than that predicted by the arithmetic mean of the left and right tilts, which demonstrates the superiority of the CNN-LSTM model in predicting height (z) at 0° by the tilt-table method in scenarios where the suspension is not locked.

Key words: vehicle engineering; tilt-table; height of center of gravity; convolutional neural network; long short-term memory networks

摘要： 车辆质心高度z是评估车辆性能及安全性的关键参数之一。侧倾试验台法测量车辆质心高度z时，在车辆悬架不锁死的情况下，由于侧倾过程中悬架变形导致质心横向位置y、质心高度z耦合变化，难以独立测量；用车辆左、右侧倾6°~12°质心高度z的算术平均值作为最终车辆0°质心高度z的测量结果，误差较大。为解决上述问题，笔者提出了一种引入物理混合损失函数的卷积神经网络(convolutional neural network, CNN)和长短期记忆网络(long short-term memory networks, LSTM)组合模型，即CNN-LSTM模型。结果表明：与Transformer、CNN和LSTM模型对比，CNN-LSTM模型的收敛速度快，预测精度高；CNN-LSTM模型预测出的0°质心高度z与左、右算术平均得到的0°质心高度z相比也更准确，体现了CNN-LSTM模型在侧倾试验台法不锁死悬架场景下预测0°质心高度z的优越性。

关键词: 车辆工程； 侧倾试验台；质心高度；卷积神经网络；长短期记忆网络