Road friction information is very important for vehicle active braking control systems such as ABS, ASR, or ESP. It is not easy to estimate the tire/road friction forces and coefficient accurately because of the nonlinear system, parameters uncertainties, and signal noises. In this paper, a robust and effective tire/road friction estimation algorithm for ABS is proposed, and its performance is further discussed by simulation and experiment. The tire forces were observed by the discrete Kalman filter, and the road friction coefficient was estimated by the recursive least square method consequently. Then, the proposed algorithm was analysed and verified by simulation and road test. A sliding mode based ABS with smooth wheel slip ratio control and a threshold based ABS by pulse pressure control with significant fluctuations were used for the simulation. Finally, road tests were carried out in both winter and summer by the car equipped with the same threshold based ABS, and the algorithm was evaluated on different road surfaces. The results show that the proposed algorithm can identify the variation of road conditions with considerable accuracy and response speed.

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The rollover of road vehicles is one of the most serious problems related to transportation safety. In this article, a novel rollover prevention control system composed of rollover warning and integrated chassis control algorithm is proposed. First, a conventional time-to-rollover warning algorithm was presented based on the 3-degree of freedom vehicle model. In order to improve the precision of vehicle rollover prediction, a back-propagation neural network was adopted to regulate time to rollover online by considering multi-state parameters of the vehicle. Second, a rollover prevention algorithm based on integrated chassis control was investigated, where the active front steering and the active yaw moment control were coordinated by model predictive control methodology. Finally, the algorithms were evaluated under several typical maneuvers utilizing MATLAB/Simulink and Carsim co-simulation. The results show that the proposed neural network time-to-rollover metrics can be a good measure of the danger of rollover, and the roll stability of the simulated vehicle is improved significantly with reduced side slip angle and yaw rate by the proposed integrated chassis control rollover prevention system. KeywordsRollover warning, neural network time to rollover, rollover prevention, integrated chassis control, model predictive control Date

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Traction control, which can be performed by different types of chassis control system, plays an important role in vehicle motion control. Since the propulsive force is actually produced by the friction between the tyre and road, information on the tyre-road friction is crucial for traction control. In this paper, a robust and effective tyre-road friction coefficient identification algorithm for straight acceleration is proposed, and a coordinative traction control method is designed by integrated usage of gear shifting control, engine control and braking control. For different driving conditions, the tyre forces were observed by a sliding-mode observer or calculated from the states of the vehicle directly, and the tyre-road friction coefficients were estimated by the recursive least-squares method or calculated from the linear characteristics between the friction coefficient and the slip ratio consequently. Based on the estimated tyre-road information, a practical and systematic coordinative traction control algorithm was designed to integrate shifting control, engine torque control and braking pressure control. Finally, the proposed methods are verified by both simulations and road tests. The results show that the estimation algorithms can identify the variation in the road conditions with considerable accuracy and response speed, and the controller successfully adjusted the slip ratios of the driving wheels in the stable region with good performances on different types of road.

An intelligent tire uses sensors to dynamically acquire or monitor its state. It plays a critical role in safety and maneuverability. Tire pressure is one of the most important status parameters of a tire; it influences vehicle performance in several important ways. In this paper, we propose a tire-pressure identification scheme using an intelligent tire with 3-axis accelerometers. As the primary sensing system, the accelerometers can continuously and accurately detect tire pressure with less electronic equipment mounted in the tire. To identify tire pressure in real time during routine driving, we first developed a prototype for the intelligent tire with three 3-axis accelerometers, and carried out data-acquisition tests under different tire pressures. Then we filtered the data and concentrated on the vibration acceleration of the rim in the circumferential direction. After analysis, we established the relationship between tire pressure and characteristic frequency of the rim. Finally, we verified our identification scheme with actual vehicle data at different tire pressures. The results confirm that the identified tire pressure is very close to the actual value.

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