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Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model's superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles. [ABSTRACT FROM AUTHOR]

To improve the robustness and adaptive capability of electronic stability control(ESC) ,the joint extended Kalman filtering technique is adopted to estimate tire coming stiffness with a 2 DOF model improved. On this basis, a joint control algorithm of linear quadratic regulator( LQR) and radial basis function neural network (RBFNN) PID control is proposed. A two-layer controller is designed, in which the upper layer uses LQR to get active yaw moment, while the lower layer controls the wheel torque allocation and variable slip ratio by RBFNN PID. 15 DOF vehicle model is used for simulation verification, and the results show that the control strategy designed better meets the control requirements of ESC and has good self-learning function and transient control capability. [ABSTRACT FROM AUTHOR]