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

• Published in: Measurement : journal of the International Measurement Confederation 2023-10, Vol.220, p.113385, Article 113385
• Main Authors: Fei, Hongzi, Liu, Bingxin, Wang, Liuping, Fan, Liyun
• Format: Article
• Language: English
• Subjects: extended Kalman filter; High-pressure common rail; Injection rate; Optimal estimation; Rail pressure
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2023.113385

•A novel estimation method for fuel injection rate of diesel engines based on the EKF is proposed.•A nonlinear injection rate dynamic model based on the rail pressure is derived.•Using appropriate state variables, an observable three-dimension state space model is built.•The estimate performance is verified under steady-state and varying working conditions.•The accuracy estimation results of injection are obtained at a wide range of operating conditions. 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%.