A dynamic modelling method for the piston-crankshaft system in an internal combustion engine, including two-dimensional oil-film forces of the piston pack, was proposed. In order to obtain the dynamic performance of this system, the dynamic equations for the components in the system were presented. Meanwhile, a radial base function neural network technology was employed to reconstruct the two-dimensional oil-film forces, which are then coupled to the presented dynamic equations for the components. The validity of the proposed modelling method for the piston-crankshaft system was demonstrated, and conclusions concerning the dynamic performance of the system were drawn.
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