This paper investigates the impact of fuel property variations on the rail pressure fluctuations in high pressure common rail (HPCR) systems and explores the possibility of indentifying fuel physical property based on the measurement of a rail pressure sensor. Fluid transients, particularly the water hammer effect, in a HPCR system are discussed and the 1D-governing equations are given. A HPCR model is developed in GT-Suite. The injectors, a three-plunger high pressure pump, and a pressure control valve are modeled in a relatively high level of detail. Five different fuels are modeled and their properties including density, bulk modulus, and acoustic wave speed are validated. Simulation results are obtained under different conditions with variable rail pressures and injection durations. The results show that natural frequency of the common rail varies with the fuel type filled in it. By applying the Fast Fourier Transform to the pressure signal, the differences of fuel properties can be revealed in the frequency domain. Since the rail pressure natural frequency is affected by the acoustic wave speed in the fuel, it can be concluded that this approach not only works for biodiesel blend level estimation, but also universally applies to the identification of various fuels and their blends as long as the acoustic wave speed in the fuel is known and the difference comparing to regular diesel is discernable.
- Dynamic Systems and Control Division
On-Board Fuel Property Identification Method Based on Common Rail Pressure Signal
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Zhao, J, & Wang, J. "On-Board Fuel Property Identification Method Based on Common Rail Pressure Signal." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 657-664. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8528
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