Performance Optimization Using ANN-SA Approach for VVA System in Diesel Engine

摘要

Diesel engine is vital in the industry for its characteristics of low fuel consumption, high-torque, reliability, and durability. Existing diesel engine technology has reached the upper limit. It is difficult to break through the fuel consumption and emission of diesel engines. VVA (Variable Valve Actuation) is a new technology in the field of the diesel engines. In this paper, GT-Suite and ANN (artificial neural network) model are established based on engine experimental data and DoE simulation results. By inputting Intake Valve Opening crake angle (IVO), Intake Valve Angle Multiplier (IVAM) and Exhaust Valve Angle Multiplier (EVAM) into the ANN Model, and by using SA (simulated annealing algorithm), the optimized results of intake and exhaust valve lift under the target conditions are obtained. According to the optimized results, the fuel consumption of BSFC (brake specific fuel consumption) can be saved by 3.9%, 0.9%, and 7.3% at three different target working conditions, respectively (1000r/min with 50% load, 2000 r/min with 35% load, and 3000r/min with 20% load. Under the three conditions, the intake valve lifts are 1.10, 0.96, 1.10 times as the original respectively and the exhaust valve lifts are 0.98,0.94 and 1.10 times respectively. In addition, by adding the secondary opening strategy of the exhaust valve lift, internal exhaust gas recycling (IEGR) can be achieved. The exhaust gas temperature increased by 9.55% (66.64K) under low-speed working condition, and fuel consumption increased slightly by 6.15%, which is also important for exhaust thermal management and external emission control in the cold start phase of diesel engines. By changing the intake and exhaust valve control, there are obvious differences in the optimal valve lift under different working conditions. Therefore, the application of the VVA system in the diesel engine is of great significance.