Dynamic Properties Modeling Analysis of the Rubber-Metal Elements for Electric Drive

The gradual increase in the volume of electric vehicles leads engineers to solve completely new problems of NVH (noise, vibration and harshness) One of the solutions is the use of rubber mounts to hold the electric motor of the vehicle’s powertrain. This article deals with a rubber mount used to insulate vibrations from the propulsion system to the rest of the vehicle. Since the electric motor produces a different spectrum of vibrations and noise than the internal combustion engine (it produces lower amplitudes, but in a wider frequency band), it is necessary to adapt the design of the rubber mounts. This article deals with the possibility of using a mathematical model that can be used in a virtual vehicle model. It compares the differences in the vibrations of the internal combustion engine and the electric motor. From the results measured on the experimental model, the parameters for the FEM model and the mathematical model were identified. At the end of the article, the results from the experiment are compared with the results from the simulations.

Configurable Sensor Model Architecture for the Development of Automated Driving Systems

Sensor models provide the required environmental perception information for the development and testing of automated driving systems in virtual vehicle environments. In this article, a configurable sensor model architecture is introduced. Based on methods of model-based systems engineering (MBSE) and functional decomposition, this approach supports a flexible and continuous way to use sensor models in automotive development. Modeled sensor effects, representing single-sensor properties, are combined to an overall sensor behavior. This improves reusability and enables adaptation to specific requirements of the development. Finally, a first practical application of the configurable sensor model architecture is demonstrated, using two exemplary sensor effects: the geometric field of view (FoV) and the object-dependent FoV.

Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles

This paper presents the results of the design, simulation, and implementation of a virtual vehicle. Such a process employs the Unity videogame platform and its Machine Learning-Agents library. The virtual vehicle is implemented in Unity considering mechanisms that represent accurately the dynamics of a real automobile, such as motor torque curve, suspension system, differential, and anti-roll bar, among others. Intelligent agents are designed and implemented to drive the virtual automobile, and they are trained using imitation or reinforcement. In the former method, learning by imitation, a human expert interacts with an intelligent agent through a control interface that simulates a real vehicle; in this way, the human expert receives motion signals and has stereoscopic vision, among other capabilities. In learning by reinforcement, a reward function that stimulates the intelligent agent to exert a soft control over the virtual automobile is designed. In the training stage, the intelligent agents are introduced into a scenario that simulates a four-lane highway. In the test stage, instead, they are located in unknown roads created based on random spline curves. Finally, graphs of the telemetric variables are presented, which are obtained from the automobile dynamics when the vehicle is controlled by the intelligent agents and their human counterpart, both in the training and the test track.