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Increasingly, advanced engine management systems incorporate high speed Digital Signal Processing (DSP) units for analyzing high-bandwidth, in-cycle signals such as those obtained from cylinder pressure, or knock sensors. In order to develop, calibrate and test the robustness of these algorithms, it is helpful to work in a simulation environment capable of simulating high-speed in-cycle data and its interaction with the engine management and DSP control strategies. Typically, however, in-cycle simulation is both deterministic and highly computationally intensive so a realistic, cyclically-varying simulation of in-cycle data is hard to generate. In this paper an alternative approach is used, based on initially recording files of high-speed, in-cycle data at different engine conditions. This database is then used to simulate the engine response as the specified engine condition varies, by playing back data from the appropriate files at each time instant.

A stochastic model for the entire pressure-time history of cycle-by-cycle cylinder pressure variations is obtained by fitting simple parametric models of cylinder pressure development to 506 cycles of continuous experimental data taken at four operating conditions. The cyclic variation is therefore encapsulated in a sequence of cyclically varying model parameters whose statistical properties then complete the stochastic description. Different model forms, (including computationally efficient linearised models), are compared for their degree of fit, and for the ease with which the statistics of the identified parameters can be defined. This approach, which typically accounts for 80-90% of the rms cyclic pressure variation, provides a more complete quantification of the phenomena than previously available, and a basis for simulating statistically identical pressure traces.