Papers by Keyword: Road Identification

Abstract: To study the problem of the road identification of the off-road vehicle, a road identification method based on on-line monitoring of the vehicle running state was put forward. The time series data of the suspension displacement and the sprung mass were monitored. The spatial power spectrum density was calculated, aiming at automatically identifying the road roughness. The vehicle speed and the longitudinal vibration acceleration were collected, aiming at identifying the terrain slope. The state of the vehicle and engine was recorded. The rolling resistance coefficient was computed, based on the output torque of the engine. Comparing this coefficient with the threshold of the soft road surface, the soft road surface could be identified. On the basis of the identification result of the road feature, the vehicle running mode can be adjusted, improving the maneuverability of the off-road vehicle.

Abstract: In order to complete road identification, dynamic identification interval is built according to the six typical road adhesion coefficients which don’t overlap with each other, using road adhesion coefficient as its parameter index under Burckhardt model. It’s concluded from simulation tests about single road and variational road based on brake single wheel model that this method is capable of managing to conduct road identification accurately and immediately, realizing automatic identification under real-time slip rate and correcting identification errors automatically.

Abstract: Accurately identifying road condition can send relevant information to the motor control system, so that control system of the motor can adjust the control strategy timely, eventually, the intelligent and optimal control of electric vehicles is realized. In this paper, according to these mathematical model, the permanent magnet synchronous motors simulation model and vehicles simulation model are proposed. Then, output torque of motor and speed of motor are served as the input of RBF neural network, which helps road condition to be identified. The simulation result shows that the road condition is well identified by proposed method based on RBF neural network.

Abstract: In pure electric vehicle and hybrid electric vehicle, the adoption of motor barking for energy recycling make its braking control more complicated. Making good use of braking energy can improve vehicle efficiency. A new method was developed to coordinate the motor regenerative braking and ABS braking. Which identify the road condition with real time basing on wheel speed information from four wheel speed sensors. Then control system decides the braking force provided by ABS system. The residual braking force is produced by motor barking to meet total braking force requirements. The two braking forces are coordinated by control system to perform brake function of vehicle.