The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

As an important application scenario of the Industrial Internet of Things (IIoT), the Internet of Vehicles can significantly improve road safety, improve traffic management efficiency and improve people's travel experience. Due to the high dynamics of the Internet of Vehicles environment, traditional resource optimization technologies cannot meet the requirements of Internet of Vehicles for dynamic communication, computing and storage resources optimization management, and artificial intelligence algorithms can adaptively obtain dynamic resource allocation schemes through self-learning. Therefore, adopting artificial intelligence techniques to optimize dynamic resource of Internet of Vehicles is the research focus of this paper. This paper first models the Internet of Vehicles resource allocation problem as a Semi-Markov Decision Process which introduces a resource reservation strategy and a secondary resource allocation mechanism. Then the reinforcement learning algorithm is used to solve the model. Thereafter, it theoretically analyzes the joint optimization of computing and communication resources, models it as a hierarchical architecture, and uses Hierarchical Reinforcement Learning (HRL) to obtain the optimal system resource allocation plan. Finally, the results of simulation experiments show that the dynamic resource allocation scheme of Internet of Vehicles based on reinforcement learning in this paper greatly improve resource utilization and user Quality of Experience (QoE) with guaranteeing system Quality of Service (QoS) compared with the traditional greedy algorithm.

This work is focused on the state of charge (SOC) estimation of a lithium-ion battery based on a nonlinear observer. First, the second-order resistor-capacitor (RC) model of the battery pack is introduced by utilizing the physical behavior of the battery. Then, for the nonlinear function of the RC model, a one-sided Lipschitz condition is proposed to ensure that the nonlinear function can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the $H_{\infty }$ method, which is formulated as linear matrix inequalities (LMIs). Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for any operating conditions. Finally, both the static and dynamic experimental cases are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter (EKF).

This paper focuses on the low-cost configuration of the lateral active suspension system for the highspeed train. First, the full-scale railway vehicle dynamics with 17-degree-of-freedom are introduced, where the actuator saturation, suspension deflection, and random and periodic track irregularities are considered. Then, based on the internal model principle, some classic active vibration control methods, e.g., the PID, positive position feedback, repetitive, and resonant controls, are unified as an internal model (IM)-based form and an IM-based resonant control is further presented. After that, to suppress various vibrations, three principles are presented to set the damping ratio and natural frequency of the IM-based resonant controller according to the properties of the controlled plant and/or the external disturbances. In consequence, an active suspension with the IM-based resonant controller is designed to improve the ride quality of the high-speed train, where the H _∞ method is used to calculate the unique gain. Moreover, the configuration of the active suspension is low cost and consists of two independent control systems, where one control system only has a pair of actuators and an accelerometer. Finally, the random and periodic track irregularities are employed to show the efficiency of the proposed resonant-control-based active suspension by comparing with the linear quadratic Gaussian (LQG) and H _∞ controllers.

The paper aims to realize a rapid online estimation of the state-of-power (SOP) with multiple constraints of a lithium-ion battery. Firstly, based on the improved first-order resistance-capacitance (RC) model with one-state hysteresis, a linear state-space battery model is built; then, using the dual extended Kalman filtering (DEKF) method, the battery parameters and states, including open-circuit voltage (OCV), are estimated. Secondly, by employing the estimated OCV as the observed value to build the second dual Kalman filters, the battery SOC is estimated. Thirdly, a novel rapid-calculating peak power/SOP method with multiple constraints is proposed in which, according to the bisection judgment method, the battery’s peak state is determined; then, one or two instantaneous peak powers are used to determine the peak power during $T$ seconds. In addition, in the battery operating process, the actual constraint that the battery is under is analyzed specifically. Finally, three simplified versions of the Federal Urban Driving Schedule (SFUDS) with inserted pulse experiments are conducted to verify the effectiveness and accuracy of the proposed online SOP estimation method.