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.
This paper presents two different ways of modeling a road vehicle for general vehicle dynamics investigation and especially to optimize the suspension geometry. Therefore a numerically highly efficient model is sought such that it can be used later in gradient-based optimization of the suspension geometry. Based on a formula style vehicle with double wishbone suspension setup, a vehicle model based on ODE-formulation using a set of minimal coordinates is built up. The kinematic loops occurring in the double wishbone suspension setup are resolved analytically to a set of independent coordinates. A second vehicle model based on a redundant coordinate formulation is used to compare the efficiency and accuracy. The performance is evaluated and the accuracy is validated with measurement data from a real formula car.
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