Design and analysis of a new brake-by-wire system using machine learning

One of the main aims of the recent research on brake-by-wire systems is to decrease mechanical components. In this paper, we propose replacing the brake pedal with a driving wheel that is fully covered by pressure braking batch sensors. The new mechanism for braking translates pressure exerted through the driver’s hands on the driving wheel to a corresponding electrical signal. A proposed design for the pressure braking batch (PBB) is made out of a mesh of conducting threads separated by a resistive sheet. To the best of our knowledge, this idea has not been raised before in other research papers. Different people have different muscle strengths and so the problem of identifying the intention of the user when pressing the PBB is tackled. For this aim, a new dataset of its kind is created by several volunteers. From each volunteer, age, gender, body mass index (BMI), and maximum pressure exerted on the driving wheel are collected. Using Weka software, the detection accuracy is calculated for a new volunteer to know the intention of his/her pressure on PBB. Among the three algorithms tried, the regression tree gives the best results in predicting the class of the pressure exerted by the volunteers

Mitigation of transient torque reversals in indirect drive wind turbine drivetrains

Bearing failure in wind turbine gearboxes is one of the significant sources of downtime. While it is well-known that bearing failures cause the largest downtime, the failure cause(s) is often elusive. The bearings are designed to satisfy their rolling contact fatigue (RCF) life. However, they often undergo sudden and rapid failure within a few years of operation. It is well-known that these premature failures are attributed to surface damages such as white surface flaking (WSF), white etching cracks (WECs) and axial cracks. In that regard, transient torque reversals (TTRs) in the drivetrain have emerged as one of the primary triggers of surface damage, as explained in this paper. The risk associated with TTRs motivates the need to mitigate TTRs arising in the drivetrain due to various transient events. This paper investigates three TTR mitigation methods. First, two existing devices, namely, the torsional tuned mass damper and the asymmetric torque limiter, are studied to demonstrate their TTR mitigation capabilities. Then, a novel idea of open-loop high-speed shaft mechanical brake control is proposed. The results presented here show that while the torsional tuned mass damper and the asymmetric torque limiter can improve the torsional vibration characteristics of the drivetrain, they cannot mitigate TTRs in terms of eliminating the bearing slip risk associated with TTRs. However, the novel approach proposed here can mitigate TTRs both in terms of improving the torque characteristic in the high-speed shaft and reducing the risk of bearing slip by actuating the high-speed shaft brake at the onset of the transient event. Furthermore, the control method is capable of mitigating TTRs with the mechanical limitations of a pneumatic actuator in terms of bandwidth and initial dead time applied to it. This novel approach allows the wind turbines to protect the gearbox bearings from TTRs using the existing hardware on the turbine

Predictive Brake Control for Electric Vehicles

This article presents a predictive braking control algorithm for electric vehicles with redundant braking actuators, composed of friction brake actuator(s) and electric motor(s). The proposed algorithm is based on a model predictive control framework and is able to optimally tackle several control goals, such as maximization of energy recuperation and wheel slip regulation, while taking into account actuation dynamics and constraints. The braking control algorithm was simulated and experimentally validated on the ROMO research vehicle. The obtained results demonstrate that, in comparison with state-of-art control techniques, the proposed MPC approach is able to: i) reduce the torque tracking error up to 60%, and ii) improve the deceleration during emergency braking up to 10%