Researching On Energy Supply And Control System Of CNG Engine

In recent years, it is urgently required to use clean and non-polluting vehicle fuel in the context of the shortage of oil resources and environment pollution intensification, especially appearing hazy weather. Natural gas is an ideal alternative to oil and diesel oil as a new and clean energy. It is considered to be one of the most promising clean fuels in current background. Energy supplication and control of natural gas engine decides the performance, fuel economy and emissions of engine. In this paper, energy supply and control of compressed natural gas is the research object. It is important significance for engineering applications that is focused researching on energy supplication and control of gas engine and researching on air-fuel ratio and ignition advance angle of engine. A natural gas supply and control system is supplied. The task is important for project application.In this paper, gas supplication system and electronic control system of fuel engine is designed. The function and characteristics of various equipments are analyzed and selected, which makes it according with the requirement of natural gas vehicle.Gas injection of fuel gas engine is determined by the air-fuel ratio. Fuzzy PID control method is used to control the transient air-fuel ratio. The control method is different for different air-fuel ratio error. The combination of fuzzy and fuzzy PID is utilized to control gas injection. When the error is large, fuzzy control is used. When error is small, fuzzy PID control is utilized. Combined two methods make air-fuel decrease rapidly, and then precisely control air-fuel ratio and gas injection.The ignition advance angle is also the important factor that affects the engine performance. The BP neural network control method is used to control ignition advance angle. The engine speed and inlet manifold pressure are BP neural network input. The ignition advance angle is BP neural network output. The appropriate neural network is defined. The BP neural network is practiced by measured data. The reasonable ignition advance angle is gotten in the different operating conditions according to the trained neural network.Finally, the MATLAB/Simulink is used to simulate air-fuel ratio and ignition advance angle. The control chart is established by Simulink and simulated. The simulation results show that the fuzzy PID can quickly stabilize air-fuel ratio near the ideal value. It verifies the effectiveness of the designed controller. The library function of BP neural network in the MATLAB is called, and different condition ignition advance angle is gotten according to trained neural. The method can reduce the calibration work and workload. It verifies the effectiveness of the designed controller.