Abstract

In this paper, the high-pressure common rail system of the marine diesel engine is taken as case study to establish a real-time simulation model of the high-pressure common rail system that can be used as the controlled object of the control system. On the premise of ensuring accuracy, the real-time simulation should also respond quickly to instructions issued by the control system. The development of the real-time simulation is based on the modular modeling method, and the high-pressure common rail system is divided into submodels, including the high-pressure oil pump, common rail tube, injector, and mass conversion. The submodels are built using the “surrogate model” method, which is mainly composed of MAP data and empirical formulas. The data used to establish the real-time simulation are not only from the empirical research into the high-pressure common rail system, but also from simulations of the high-pressure common rail system undertaken in AEMSim. The data obtained from this real-time simulation were compared with the experimental data to verify the model. The error in fuel injection quality is less than 5%, under different pressures and injection durations. In order to carry out dynamic verification, the PID control strategy, the model-based control strategy, and the established real-time simulation are all closed-loop tested. The results show that the developed real-time simulation can simulate the rail pressure wave caused by cyclic injection according to the control signal, and can feedback the control effect of different control strategies. Through verification, it is clear that the real-time simulation of the high-pressure common rail system can depict the rail pressure fluctuation caused by each cycle of fuel injection, while ensuring the accuracy and responsiveness of the simulation, which provides the ideal conditions for the study of a rail pressure control strategy.

Highlights

  • Emission regulations for marine diesel engines are increasingly stringent [1]

  • Using the idea of the surrogate model, the real-time simulation model can reduce the amount of calculation required and ensure high accuracy, and the error is less than 5%

  • According to the data provided by the performance simulation, the injector submodel can receive the fuel injection timing and the fuel injection duration signal provided by the control system

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Summary

Introduction

Emission regulations for marine diesel engines are increasingly stringent [1]. The high-pressure common rail system is one of the main measures taken to purify marine diesel engines [2]. Fadila et al analyzed the effect of a multiple injection control strategy on the temperature and HC emissions, after establishing the average model of a diesel engine [10]. In order to provide a controlled object for the development of a high-pressure common rail electronic control system of marine diesel engines, it is necessary to study the real-time simulation of the high-pressure common rail system. The contribution of this paper is to establish a real-time simulation model of a marine diesel engine high-pressure common rail system, provide controlled objects for the development of marine diesel engine control systems, reduce development costs, and reduce potential safety hazards. The developed real-time simulation of the marine high-pressure common rail fuel system can provide controlled simulation objects for the closed-loop testing of fuel injection and the rail pressure regulation functions. Energies 2021, 14, 5481 of the electronic control system, as well as provide technical support for further improving the integrity of the function of the real-time simulation of marine diesel engine

Framework of Real-Time Simulation for a High-Pressure Common Rail System
Determination of the Simulation Performance of the High-Pressure Common
Principles of the Real-Time Simulation Creation
Fuel Injector Submodel
High-Pressure Oil Pump Submodel
Common Rail Pipe Submodel
Submodel of Temperature Calculation
Submodel of Mass Calculation
Verification of the Real-Time Simulation
Static Verification
Dynamic Verification
Findings
Conclusions
Full Text
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