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PN(Particle Number) emission limits are more stringent for gasoline vehicles in Chinese VI emission standards (6×1011 #/km). A EEPS engine exhaust particle size spectrometer was employed to characterize the effects of injection strategies on particulates emissions from a turbocharged gasoline direct injection (GDI) engine. The effects of operating parameters (injection pressure, second injection ratio and second injection end time) on particle diameter distribution and particle number density of emission was Investigated. The experimental result indicates that the quantity of particles decrease with the increase of injection pressure obviously, especially at high load including the 20% reduction of the particle number density. When the engine is at low load, the accumulation mode particle emissions are higher than the nucleation mode particle emissions compared with high load, which present opposite results. The second injection can restrain engine knock at low speed.

Reinforcement learning is a promising approach to solve the energy management for hybrid electric vehicles. In this paper, based on the DQN (Deep Q-Network) reinforcement learning algorithm which is widely used at present, double DQN, dueling DQN and learning from demonstration are integrated; states, actions, rewards and the experience pool based on the characteristics of series-parallel multi-speed hybrid powertrain are designed; the hybrid energy management strategy based on D4QN (Double Dueling Deep Q-Network with Demonstrations) algorithm is established. Based on the training results of D4QN algorithm, multi-parameter analysis under state and action space, HCU (Hybrid control unit) application and MIL (Model in-loop) test research are conducted.

A DMS500 engine exhaust particle size spectrometer was employed to characterize the effects of injection strategies on particulate emissions from a turbocharged gasoline direct injection (GDI) engine. The effects of operating parameters (injection pressure, secondary injection ratio and secondary injection end time) on particle diameter distribution and particle number density of emission were investigated. The experimental result indicates that the split injection can suppress the knocking tendency at higher engine loads. The combustion is improved, and the fuel consumption is significantly reduced, avoiding the increase in fuel pump energy consumption caused by the 50 MPa fuel injection system, but the delayed injection increases particulate matter emissions.

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.