Optimization of fuel injection timing and ignition timing of hydrogen fueled SI engine based on DOE-MPGA

For the port injection hydrogen-fueled spark ignition (SI) engine modified from the Jialing JH600 gasoline engine, the hydrogen injection timing and ignition timing of the hydrogen SI engine are comprehensively optimized on the AVL-FIRE software based on the combina-tion of design of experiment (DOE) and multiple population genetic algorithm (MPGA). Firstly, the effects of hydrogen injection timing and ignition timing on the performance of the hydrogen engine are investigated through the full factors design of experiment scheme, and then the optimum intervals of them are determined respectively. Secondly, the uniform design of experiments scheme is arranged, and the regression functions of indicated power, indicated thermal efficiency and emissions of the hydrogen engine under different working conditions are fitted. Then, the comprehensive optimization model is constructed. Finally, the optimum hydrogen injection timing and ignition timing of the hydrogen engine under different working conditions are solved based on MPGA. The re-sults show that, compared with the standard genetic algorithm (SGA), the MPGA has faster convergence speed and higher convergence accuracy. In addition, as the load increases from low to high, the optimum hydrogen injection timing is advanced and the rate of variation increases from 0.6% to 1.1%, the optimum ignition timing is delayed and the rate of variation decreases from 29.1% to 17.8%. (c) 2022 Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC.

Automated summary

The hydrogen injection timing and ignition timing of the port injection hydrogen-fueled spark ignition (SI) engine modified from the Jialing JH600 gasoline engine were optimized using a combination of design of experiment (DOE) and multiple population genetic algorithm (MPGA) on AVL-FIRE software. The effects of hydrogen injection timing and ignition timing on the performance of the hydrogen engine were investigated, and their optimum intervals were determined. Regression functions were fitted for indicated power, indicated thermal efficiency, and emissions, and a comprehensive optimization model was constructed. MPGA showed faster convergence speed and higher convergence accuracy compared to standard genetic algorithm (SGA). The optimum hydrogen injection timing advanced and the rate of variation increased as the load increased, while the optimum ignition timing was delayed and the rate of variation decreased.