China
Africa
Explore chapters and articles related to this topic
Anti-lock brakes and traction control
Published in M.J. Nunney, Light and Heavy Vehicle Technology, 2007
During moving off and acceleration, a traction control system performs a similar safety function to anti-lock braking by preventing the driving wheels from slipping, which therefore helps both to maintain directional control and to improve traction under adverse driving conditions, especially on road surfaces that are slippery on one side of the vehicle only. Since a traction control system may be regarded as a logical but inverse development of anti-lock brakes and can therefore utilize some of the same technology, it is perhaps to be expected that this type of system was pioneered by Robert Bosch working in conjunction with Mercedes-Benz, their anti-slip regulation system being introduced in 1987 and known as Antriebs-Schlupf-Regelung (ASR). Other manufacturers have since introduced traction control systems that may similarly be integrated with anti-lock hydraulic and now air brake systems.
Autonomous Vehicles
Published in Iqbal Husain, Electric and Hybrid Vehicles, 2021
The primary hardware for traction control and ABS are mostly the same. Sensors used for traction control are wheel speed sensors which sense changes in its speed due to loss of traction. When the traction control system detects that one or more driven wheels are spinning significantly faster than another, it activates the ABS to apply brake to the wheels that are spinning with reduced traction. This braking action on slipping wheels will cause power transfer to the wheels with traction due to the mechanical action of the differential to restore vehicle stability.
Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies
Published in Traffic Injury Prevention, 2023
The research presented in this article is based on the actual operation data of autonomous bus No. 45 in Shanghai, China. Autonomous bus No. 45 is equipped with assisted driving systems capable of collecting information affecting the safety of the bus’s operation, considering human, vehicle, road, and environmental factors. Firstly, the throttle misstep protection system can collect the accelerator pedal opening data. Secondly, the traction control system can collect data on the speed, revolutions, and GPS direction. Thirdly, the GPS module in the lane departure warning system can collect the latitude and longitude data. Finally, the collision mitigation braking system is able to collect the relative speed, relative lateral distance, and relative longitudinal distance data between the autonomous bus and surrounding vehicles or obstacles, as well as the risk mode information. Research data include the operation data of autonomous bus No. 45 from June 4, 2020, to June 9, 2020. Eight indicators from the data that affect the safety of autonomous bus operation include latitude x_1, longitude x_2, GPS direction x_3, speed x_4, revolution x_5, relative speed x_6, relative lateral distance x_7, and relative longitudinal distance x_8. The risk mode of the autonomous bus can also be obtained from the data, where m_0 means no risk, m_1 means high risk, and m_2 means low risk.
Modelling and stability analysis of a longitudinal wheel dynamics control loop with feedback delay
Published in Vehicle System Dynamics, 2022
Adam Horvath, Peter Beda, Denes Takacs
These issues will be investigated using a wheel traction control system as an example. The article is organised as follows: Section 2 contains the description of the continuous time dynamical model of the control loop. Firstly, the equations of motion of the wheel is presented, that is followed by the dynamic and steady state brush tyre models. Finally, a PID controller and the feedback delay is presented. Section 3 deals with the hybrid model of the control loop, in which sampling and different methods of numerical differentiation are considered. Stability analysis is performed using the method of semi-discretisation. Section 4 contains the results concerning the stability maps and performance of the control loop, and some numerical simulation results. Finally, section 5 is about the conclusions of the study.
Dynamic performance of locomotive electric drive system under excitation from gear transmission and wheel-rail interaction
Published in Vehicle System Dynamics, 2022
Ziwei Zhou, Zaigang Chen, Maksym Spiryagin, Peter Wolfs, Qing Wu, Wanming Zhai, Colin Cole
In recent years, there have been many studies performed in the field of locomotive control systems. It is difficult to imagine that modern electric locomotives could carry out various transportation tasks safely, smoothly and reliably, without a good design on the control subsystems and mechanical subsystems. A dynamic co-simulation model is established for a railway locomotive in this section by considering both the electrical subsystem and the mechanical subsystem, as shown in Figure 1. In this figure, the meaning of the symbols are explained as: vx – locomotive speed, m/s; N – the throttle setting in notches [29]; sest – estimated slip; aest – estimated angular acceleration of wheel, m/s2; sthr – slip threshold value; athr – acceleration threshold value, m/s2; ΔT – reduced torque, N·m; Fref – reference force dependent on the notch position, N; Tref – reference torque dependent on Fref, N·m; – reference torque generated by the traction control system, N·m; Te – the output torque of traction motor, N·m; wm – angular velocity of traction motor, rad/s; ww – angular velocity of wheel, rad/s; the other parameters are signal parameters of the traction control system.