面向燃烧闭环控制的天然气掺氢发动机 CA50 预测

To explore the method of improving engine efficiency and reducing emissions, the impact of combustion closed-loop control key parameters CA50 on the combustion and emissions of a hydrogen-enriched compressed natural gas (HCNG) engine was experimentally studied, and CA50 based on the experimental results was statistically analyzed. Meanwhile,the particle swarm optimization(PSO)back-propagation neural network (BPNN) algorithm was applied to the prediction of CA50,and the influence of hybrid strategy optimization on the performance of PSO-BPNN model was investigated. Results show that:CA50 has a significant impact on the combustion characteristics and emissions of the HCNG engine; CA50 obeys the normal distribution and has no auto-correlation,so it can be used as the feedback parameter of combustion closed-loop control;the CA50 prediction model established by PSO-BPNN method has the high prediction performance and good generalization ability,with the average absolute error of 0.25°CA and the correlation coefficient of more than 0.997;the hybrid strategy can significantly improve the convergence speed of the model without reducing prediction accuracy,with the CPU running time reduced by up to 73.02%.