In this work, frequency response functions (FRFs) are used for estimation of cycle-by-cycle cylinder pressure and indicated torque waveform, using crankshaft speed fluctuations. The FRFs are mapped as a function of the discrete Fourier transform of engine speed, mean speed and manifold pressure using a multilayer neural network. The accuracy of the model is analysed using some of the parameters derived from the cylinder pressure. These include the indicated mean effective pressure and peak pressure. The load torque on the engine is also estimated using a closed-loop observer. The model is tested on a test rig consisting of single-cylinder engine coupled with an eddy current dynamometer. The results show that the model is suitable for the estimation of cylinder pressure and other variables related to it at the operating points where the cyclic variations are within a driveability limit.
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