Regression prediction of hydrogen enriched compressed natural gas (HCNG) engine performance based on improved particle swarm optimization back propagation neural network method (IMPSO-BPNN) - Duan, Hao; Yin, Xiaojun; Kou, Hailiang; Wang, Jinhua; Zeng, Ke; Ma, Fanhua (Fuel (Guildford), 2023)
•The maximum torque and minimum fuel consumption occur at MBT with relatively low NOx.•IMSPO-BPNN model has the highest prediction accuracy and the best generalization ability.•The IMPSO-BPNN method has great superiority in operation speed.•IMSPO-BPNN method is a suitable choice for the prediction of HCNG engine performance. Artificial neural network (ANN) methods have been rapidly developed and applied in solving nonlinear small sample problems. In this paper, an improved particle swarm algorithm optimized back propagation neural network (IMPSO-BPNN) method was proposed and used for the regression analysis and prediction of 20% (volume fraction) hydrogen enriched compressed natural gas (HCNG) engine performance. Meanwhile, various ANN and support vector machine (SVM) methods were also utilized for a comparative study. The experimental results show that the HCNG engine has the highest combustion efficiency, the maximum output torque and the minimum brake specific fuel consumption (BSFC) when operating at the maximum brake torque (MBT) timing, and the brake specific NOx (BSNOx) is also at a relatively low level. Through the comparison of multiple methods, the prediction accuracy and generalization ability of the IMPSO-BPNN model are the best overall. For example, the mean absolute percentage error (MAPE) of the optimal IMPSO-BPNN model (0.771%) is 5.85%, 12.62%, 17.96%, 7.57% and 7.88% less than that of the PSO-BPNN, GA-BPNN, BPNN, PSO-SVM and GA-SVM models (0.819%, 0.882%, 0.940%, 0.834% and 0.837%), respectively; and the correlation coefficient (R) is also higher than that of other models (0.99986). Secondly, the method shows visible superiority in the temporal dimension compared with the methods optimized by genetic algorithm and SVM method. For example, the CPU running time of the optimal IMPSO-BPNN model is reduced by 1773.32%, 149.74% and 1231.34% compared to that of the optimal GA-BPNN, PSO-SVM and GA-SVM models, respectively. Since the IMPSO-BPNN method is a flexible and general method, it is a new idea for the study of engine electronic control calibration tools.