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This paper describes algorithms that can recognize and track the engine crankshaft position for arbitrary cam- and crank-shaft tooth wheel patterns in both steady-state and transient operating conditions. Crankshaft position tracking resolution is adjustable to accommodate different application requirements. The instantaneous crankshaft position information provided by the position tracking module form the basis for crankshaft angle domain (CAD) engine control and measurement functions such as precise injection / ignition controls and on-line cylinder pressure CAD analyses. The algorithms described make reconfiguration of the tracking module for different and arbitrary cam- and crank-shaft tooth wheel patterns very easy, which is valuable especially for prototyping engine control systems. The effectiveness of the algorithms is shown using test engines with different cam and crank signal patterns.

Tremendous amount of useful information can be extracted from the cylinder pressure signal for engine combustion control. However, the physical cylinder pressure sensors are undesirably expensive and their health need to be monitored for fault diagnostic purpose as well. This paper presents the results of the development of a virtual cylinder pressure sensor (VCPS) with individual variable-oriented independent estimators. Two neural network-based independent cylinder pressure related variable estimators were developed and verified at steady state. The results show that these models can predict the variables correctly compared with the extracted variables from the measured physical cylinder pressure sensor signal. Good generalization capabilities of the developed models are observed in the sense that the models work well not only for the training data set but also for the new inputs that they have never been exposed to before.