Cylinder Pressure-Based Spark Advance Control for SI Engines

Abstract

The introduction of inexpensive cylinder pressure sensors provides new opportunities for precise engine control. This paper presents a spark advance control strategy based upon cylinder pressure in spark ignition engines. It is well known that the location of peak pressure(LPP) reflects combustion phasing and can be used for controlling the spark advance. The well-known problems of the LPP-based spark advance control method are that many samples of data are required and there is loss of combustion phasing detection capability due to hook-back at late burn conditions. To solve these problems, a multi-layer feedforward neural network is employed. The LPP and hook-back are estimated, using the neural network, which needs only five output voltage samples from the pressure sensor. The neural network plays an important role in mitigating the A/D conversion load of an electronic engine controller by increasing the sampling interval from 1° crank angle (CA) to 20° CA. A proposed control algorithm does not need a sensor calibration and pegging (bias calculation) procedure because the neural network estimates the LPP from the raw sensor output voltage. The estimated LPP can be regarded as a good index for combustion phasing, and can also be used as an MBT control parameter. The feasibility of this methodology is closely examined through steady and transient engine operations to control individual cylinder spark advances. The experimental results have revealed a favorable agreement of optimal combustion phasing in each cylinder.