基于静动态分离的MAF传感器动态非线性建模

MAF sensor dynamic non-linearity modeling based on static-dynamic characteristic separation

热膜式空气质量流量传感器广泛用于汽车发动机,但是,在有些工况下,传感器的动态响应速度不能满足控制的要求。在研究中发现,传感器的动态特性有明显的非线性。本文基于传感器的静态和动态实验,提出了基于静动态分离的Hammer-stein两步法的动态非线性建模方法,建立了大小流量和正负阶跃统一的数学模型,更加准确地描述传感器的动态非线性特性。

Hot-film mass air flow （MAF） sensor is widely used in automotive engines. The dynamic response of the MAF sensor, however, cannot satisfy the control requirements under some operating conditions. Our studies found that the sensor dynamic characteristic has obvious non-linearity. Therefore a two-stage identification modeling method is proposed based on static-dynamic characteristic separation. According to the sensor static and dynamic experimental data, the uniform mathematical model in Hammerstein form is built up to describe the non-linear dynamic characteris- tics of the sensor more accurately, which can be applied to large/small flow rates and positive/negative step inputs.