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

Abstract

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 VW eGolf 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.

Data Driven prediction of forced nonlinear vibrations using stabilised Autoregressive Neural Networks

“…Typical applications occur whenever the materials exhibit nonlinear properties, or specific geometry leads to nonlinear behaviour. The analysis of vibrations that experience an external excitation is of particular relevance, such as vibrations in vehicles under road excitation, for instance [5]. In practical applications, a large amount of measurement data is often collected, which inherently contains the nonlinear system properties in an aggregated manner.…”