A control strategy for efficient slip ratio regulation of a pneumatic brake system for commercial vehicles. (June 2022)

• Record Type: Journal Article
• Authors: Kim, Jayu; Kwon, Baeksoon; Park, Youngnam; Cho, HyunJong; Yi, Kyongsu
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 7(2022)
• Page Start: 1546
• Page End: 1567
• Publication Date: 2022-06
• Subjects: Antilock braking system -- traction control system -- switched control -- limit cycle control -- pneumatic brake system -- wheel dynamic -- experimental validation; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/09544070211038466
• Languages: English
• ISSNs: 0954-4070

Abstract

This paper presents a control strategy for efficient slip ratio regulation of a pneumatic brake system for commercial vehicles. A model-based estimator for brake pressure estimation has been developed. The braking torque applied to the wheel has been computed using the estimated brake pressure for the control of the wheel slip both in braking and traction situations. The vehicle velocity and wheel slip ratio estimation algorithms have been designed using only wheel speed sensors. The proposed slip regulation algorithm has also been successfully implemented for the antilock braking system (ABS) and traction control system (TCS). In ABS, the slip ratio and wheel acceleration are stabilized by a limit cycle control of the braking pressure. The TCS has been implemented by combining engine torque control and pneumatic brake pressure control. The brake controller is based on the valve switched control that incorporates the wheel dynamics and valve on/off characteristics. The ABS and TCS algorithms are integrated into the slip regulation algorithm to reduce the computation load of an Electrical Control Unit (ECU). Four-wheel independent slip monitoring and slip ratio control algorithms have been implemented on the ECU, and their performance has been investigated via both computer simulations and vehicle tests. Both results show that the proposed algorithms enhance the acceleration and braking performance without vehicle acceleration information. Moreover, the proposed split-mu strategy has improved the lateral stability during braking, and the acceleration performance during accelerating on the split-mu road. It has been shown via vehicle tests that, compared to the reference commercial algorithm, the braking distance was reduced by more than 4% on the split-mu and low-mu roads, and the acceleration performance was improved by 7.9% on the split-mu road.