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In the literature, a wide range of Mean Value Engine Models are used in the simulation and control of reciprocating engines. These models are often underpinned by a number of implicit assumptions, which determine the model structure and system states. Systematic model reduction approaches have been developed to avoid these assumptions, where high order models are reduced using singular perturbation techniques, eliminating states operating on irrelevant time-scales. While this framework allows the elimination of states based on sufficiently small perturbation parameters, a systematic method of identifying non-dimensional perturbation parameters has not yet been proposed. The development of a rigorous method to identify non-dimensional time scales present in the model is a natural and powerful extension to the existing approach.