Transport and Logistics Competence Centre, Transport Engineering Faculty, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania.
Department of Mobile Machinery and Railway Transport, Transport Engineering Faculty, Vilnius Gediminas Technical University, 08101 Vilnius, Lithuania.
Sensors (Basel). 2021 Oct 27;21(21):7139. doi: 10.3390/s21217139.
With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long-Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.
随着汽车行业向自动驾驶方向发展，传感技术在实现技术方面变得越来越重要。虚拟传感器允许来自各种车辆传感器的数据融合，并提供难以或以其他方式进行测量或对连续检测有需求的测量的预测。本文讨论了用于车辆悬架控制的虚拟传感，其中需要每个车辆角的非簧载质量的相对速度的信息。相应的目标可以确定为具有多输入序列输入的回归任务。假设最先进的双向长短时记忆（BiLSTM）方法可以解决它。在本文中，通过训练神经网络模型提出并开发了虚拟传感器。使用 IPG 汽车制造商中经过实验验证的整车模型进行了仿真。仿真提供了参考数据，这些数据用于神经网络（NN）训练。使用涵盖 26 种情况的广泛数据集来获得训练、验证和测试数据。使用均方根误差作为指标的贝叶斯搜索来选择最佳神经网络结构。最佳网络由 167 个 BiLSTM、256 个全连接隐藏单元和 4 个输出单元组成。与参考信号相比，预测信号的误差直方图和频谱分析。结果表明，基于神经网络的虚拟传感器在估计车辆非簧载质量相对速度方面具有很好的适用性。
