Kalman filter-based estimation method for degraded high-pressure common rail system

Fuel injection characteristics are crucial parameters influencing engine performance. Over prolonged operation, components of common rail systems may gradually degrade, leading to abnormal injection performance. Real-time acquisition of injection information is essential for timely optimizing injection strategies. This study presents an adaptive estimation method based on the Kalman filter using rail pressure fluctuations to enable real-time injection perception throughout the lifecycle. A degradation factor is introduced to quantify the impact of structural degradation on injection characteristics. Considering degradation's slow-varying nature, a nonlinear time-varying state-space model is constructed with rail pressure and degradation factor as output variables, ensuring both observability and feasibility. Due to the multi-rate sampling of these outputs, a dual time-scale extended Kalman filter is designed, and a sequential update strategy is incorporated to further reduce complexity by eliminating matrix inversion. Simulations investigate the effects of various degradation modes and levels on injection rate and verify the method's effectiveness. Results show that the proposed method accurately estimates injection rate and injection volume, with estimation errors below 3.5 % and strong adaptability in coupled degradation scenarios. Experimental validation with a different type of injector and rail pipe further confirms the method's generalizability and practical value for real-time monitoring and health assessment.