Study on prediction model of diesel engine with regulated two-stage turbocharging system based on hybrid genetic algorithm-particle swarm optimization method at different altitudes

Abstract

ABSTRACT Aiming at the problem of complicated mechanism modeling for regulated two-stage turbocharging (RTST) system of diesel engine, a hybrid genetic algorithm–particle swarm optimization (GA-PSO) method was applied to optimize back propagation neural network (BPNN) performance prediction model of diesel engine. First, using a high-altitude (low-pressure) simulation test bench for diesel engines, a full-load performance test of RTST diesel engine at different altitudes was carried out, and the regulation rules of diesel engine intake characteristics and two-stage turbocharger (TST) matching characteristics with altitude were obtained. Based on the test data, the BPNN is used to establish the boost pressure prediction model of the RTST system of the diesel engine. For the problem of slow convergence and low prediction accuracy of the BPNN, adaptive improvement measures such as momentum and learning rate are introduced, and the GA-PSO optimizes the weights and thresholds of BPNN. The performance comparison shows that hybrid GA-PSO is superior to particle swarm optimization (PSO) and genetic algorithm (GA) in terms of evolution speed and convergence accuracy. Genetic algorithm–particle swarm optimization–back propagation neural network (GA-PSO-BPNN) model has higher prediction accuracy and higher accuracy than particle swarm optimization–back propagation neural network (PSO-BPNN) model and genetic algorithm–back propagation neural network (GA-BPNN) model. Compared with the original model, the root-mean-square error of the model is reduced by 50%, the mean error percentage (MEP) is reduced by 38%, and the sum of the squared errors is reduced by 79%. The performance verification of the GA-PSO-BPNN model shows that the MEP of the model prediction results is 5.5%, and the GA-PSO-BPNN model has higher prediction accuracy.