The emergence of new electric vehicle (EV) corner concepts with in-wheel motors offers numerous opportunities to improve handling, comfort, and stability. This study investigates the potential of controlling the vehicle's corner positioning by changing wheel toe and camber angles. A high-fidelity simulation environment was used to evaluate the proposed solution. The effects of the placement of the corresponding actuators and the actuation point on the force required during cornering were investigated. The results demonstrate that the toe angle, compared to the camber angle, offers more effect for improving the vehicle dynamics. The developed direct yaw rate control with four toe actuators improves stability, has a positive effect on comfort, and contributes to the development of new active corner architectures for electric and automated vehicles.

This article introduces a methodology for conducting comparative evaluations of vibration-induced discomfort. The aim is to outline a procedure specifically focused on assessing and comparing the discomfort caused by vibrations. The article emphasizes the metrics that can effectively quantify vibration-induced discomfort and provides insights on utilizing available information to facilitate the assessment of differences observed during the comparisons. The study also addresses the selection of appropriate target scenarios and test environments within the context of the comparative evaluation procedure. A practical case study is presented, highlighting the comparison of wheel corner concepts in the development of new vehicle architectures. Currently, the evaluation criteria and difference thresholds available allow for comparative evaluations within a limited range of vehicle vibration characteristics.

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 un-sprung mass relative velocity.