Prediction and decision of free-piston linear generator on starting process for multi-fuel adaptability

Free-piston linear generators, due to their variable compression ratio, are considered to possess excellent multi-fuel adaptability. Further, fuel-flexible is a weighty research topic for addressing energy source limitations, meeting powertrain generalization requirements, and achieving energy savings and emission reductions. The initial concern to solve is the rapid starting of various fuels within the same free-piston linear generator. This study aims to introduce a mechanical resonance starting performance prediction model based on an artificial neural network and provides a control decision method based on single-objective optimization with soft constraints to achieve flexible starting of a variety of fuels. This paper also assessed the starting performance using the free-piston linear generator prototype and conducted a comprehensive parameterized analysis to investigate the influence of parameters and control variables on starting performance. Using the starting decision method proposed, the control variable values for ethanol starting were derived on the prototype, resulting in driving current at 3.5A, intake air mass flow at 95 standard liters per minute, and intake air temperature at 335 K. This decision value effectively met the predefined requirements, successfully igniting and starting the prototype with ethanol. This outcome serves as a robust validation of the fuel-flexible capabilities within free-piston linear generator systems.