Optimal estimation of injection rate for high-pressure common rail system using the extended Kalman filter

Abstract

This paper presents an innovative estimation method of the fuel injection rate based on the rail pressure measurement signal for a high-pressure common rail system. A dynamic mathematical model of the injection process is constructed. To meet the requirement of the estimator design, a nonlinear state space model with three state variables is derived. On this basis, an optimal estimation method for the injection rate is proposed based on the extended Kalman filter, and the impact of noise covariance matrices is thoroughly examined. The results demonstrate that the proposed method enables fast convergence of the estimated injection rate. The coefficient R2 values between the estimated and the actual injection rate curves exceed 94%, and the estimated errors of the injection volume are within 5%.