Predictability of Vibration Loads From Experimental Data by Means of Reduced Vehicle Models and Machine Learning

Nowadays electric cars are in the spotlight of automotive research. In this context we consider data based approaches as tools to improve and facilitate the car design process. Hereby, we address the challenge of vibration load prediction for electric cars using neural network based machine learning (ML), a data-based frequency response function approach, and a hybrid combined model. We extensively study the challenging case of vibration load prediction of car components, such as the traction battery of an electric car. We show using experimental data from Fiat 500e and VWeGolf cars that the proposed ML approach is able to outperform the classical model estimation by means of ARX and ARMAX models. Moreover, we evaluate the performance of a hybrid-ML concept for combination of ML and ARMAX. Our promising results motivate further research in the field of vibration load prediction using machine learning based approaches in order to facilitate design processes.