This paper presents a method for predicting the operating parameters of an FPLG
based on the demanded power. First, a 1D FPLG model was developed in AMESim,
based on established structural principles and a characterization of stable
operation. The model was validated at specific operating points using an
experimental prototype. Due to the limited number of available operating points
in the prototype, the model boundaries were explored, and the influence of input
variables was analyzed. Ultimately, injected mass, spark timing, and injection
timing were selected as the primary control parameters. Further analysis
examined how variations in these parameters affect the system’s steady-state
operation, and the relationship between input parameters, output efficiency, and
power was established. Based on this relationship, two rules—optimal efficiency
and stable operation—were proposed. These rules were integrated with a
three-layer coupled machine learning model to form an FPLG-specific prediction
and control strategy. Finally, the effectiveness of the machine learning
predictions was validated using a demand power curve based on the FTP75 test
driving cycle.