Parameters self-learning of solenoid valve for wheel pressure estimation

Abstract

As the most important component of the braking pressure regulation circuit, apply valve directly affect the accuracy of pressure control and estimation, which in turn affects the performance of vehicle dynamics control. However, the not exactly same parameters in the manufacturing and the parameters change with wear and tear will introduce large errors in the model-based pressure estimation. In this paper, the electromagnetic model and hydraulic model of apply valve are proposed and illustrated, which is the basis for pressure estimation and air gap learning. The particle swarm optimization (PSO) is presented for the self-learning of apply valve air gap, and the pressure estimation algorithm is presented based on the learned air gap. The proposed model and the algorithm are verified in the vehicle test and Amesim simulation. The results show that the proposed algorithm achieves the estimation of air gap within the error of [Formula: see text], and thus reduces pressure estimation error to smaller than [Formula: see text].