The complex hydropneumatic electromagnetic coupling structure of the dual-fuel
injector leads to its complicated injection process. The unknown problem of fuel
injection characteristics limits the injector design and optimization process of
combustion efficiency. Therefore, the scientific study of dual-fuel injection
mechanism and online identification method is the key to grasping the diesel-gas
coupled injection mechanism, and an important theoretical basis for advanced
closed-loop control.
In this study, an identification method for the time characteristics of the
dual-fuel injector injection process is based on the injector inlet pressure,
which can be applied to the diesel-natural gas co-direct injection engine.
First, the cause and transfer process of diesel injection pressure waves were
analyzed based on the Riemann invariant theory. In addition, the identification
method of diesel injection time characteristics is proposed by combining the
characteristics of the derivative diesel pressure signal and the pressure wave
of an expansion wave transmitted from the injector to the diesel rail
(W
2) and a compression wave transmitted from the injector to the diesel
rail (W
4), which are caused by the upstroke and downstroke process of the
needle valve. Second, the gas injection time characteristics are highlighted on
the frequency domain by the short-time Fourier transform (STFT), and the gas
injection time characteristics are located in the time domain. To adapt to the
processing capacity of the engine electronic control unit (ECU), the mean
instantaneous frequency (MIF) is used as the representative
frequency to reduce the signal dimension. Based on the MIF
signal, the time characteristics can be identified according to the zero and
local maximum. Finally, the identification method is validated by a
multi-function measurement system. The results show that the error of
identification method of diesel injection time characteristics is 0.02 ms and,
for the gas injection process, is 0.015 ms. Therefore, the method can identify
the time characteristics of the dual-fuel injection process, and the accuracy is
pretty good.
