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This paper presents a crankshaft speed fluctuation model based dynamic neural network misfire detection method to achieve high detection performance and compact network size. In this method, a dynamic neural network with output feedback is utilized to model an inverse system from the engine crankshaft speed signal to the firing event signal. The engine misfire detection is based on the output of the inverse system given the input of engine speed signal. Test results for a 4-cylinder engine show its promising capability of misfire detection even for the low sampling rate data under various engine operating conditions and misfire patterns.

An optimal idle speed control (ISC) system for an automotive engine is introduced in this paper. The system is based on a non-linear model including time delay. This model is linearized at the nominal operating point. The effect of the time delay on control is compensated by prediction. This methodology is applied to a Chrysler 2.0 liter 4-cylinder SOHC (Single Overhead Cam) engine. All of the unknown parameters of the model are identified by using the normal operating data from the test engine. Based on these identified parameters, an optimal controller was designed and implemented using a rapid prototyping system. Numerous experiments of the optimal controller were carried out at the Chrysler Technology Center in Auburn Hills, Michigan. The performance was compared to that of the existing controller. The results showed that the optimal controller has the capability to effectively control the engine idle speed under a variety of accessory loads and disturbances.

Engine misfires cause a negative impact on exhaust emissions. Severe cases could damage the catalyst system permanently. These are the basic reasons why CARB (California Air Resources Board) mandated the detection of engine misfires in their OBD II (On-Board Diagnostics II) regulations. For the last several years, automobile manufacturers and their suppliers have been working diligently on various solutions for the “Misfire Detection” challenge. Many have implemented a solution called “Crankshaft Velocity Fluctuation” (CVF), which utilizes the crank sensor input to calculate the variation of the crankshaft rotational speed. The theory is that any misfires will contribute to a deceleration of the crankshaft velocity due to the absence of pressure torque. This approach is marginal at best due to the fact that there could be many contributors to a crankshaft velocity deceleration under various operating conditions. To sort out which is a true misfire is a very difficult task.