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To solve the problem of serious roller wear and improve the smoothness of the sliding door motion process, the rigid-flexible coupling multi-body model of the vehicle sliding door was built in ADAMS. Force boundary conditions of the model were determined to meet the speed requirement of monitoring point and time requirement of door opening-closing process according to the bench test specification. The results of dynamic simulation agreed well with that of test so the practicability and credibility of the model was verified. In the optimization of the ride comfort of the sliding door, two different schemes were proposed. The one was to optimize the position of hinge pivots and the other was to optimize the structural parameters of the middle guide. The impact load of lead roller on middle guide, the curvature of the motion trajectory and angular acceleration of the sliding door centroid were taken as optimization objectives.

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.

Carbon fiber reinforced plastic (CFRP) composites have gained particular interests due to their high specific modulus, high strength, lightweight and resistance to environment. In the automotive industry, numerous studies have been ongoing to replace the metal components with CFRP for the purpose of weight saving. One of the significant benefits of CFRP laminates is the ability of tailoring fiber orientation and ply thickness to meet the acceptable level of structural performance with little waste of material capability. This study focused on the concurrent optimization of ply orientation and thickness for CFRP laminated engine hood, which was based on the gradient-based discrete material and thickness optimization (DMTO) method. Two manufactural constraints, namely contiguity and intermediate void constraints, were taken into account in the optimization problem to reduce the potential risk of cracking matrix of CFRP.

The Proton Exchange Membrane Fuel Cell (PEMFC) is the most widely used engine in fuel cell vehicles. For PEMFC, whether the supply of oxygen for cathode is adequate or not is a critical factor for its net output power and service life, and the proper control of air supply mass flow and pressure can effectively improve its system performance and efficiency. At present, fuel cells need to reduce the mass and volume and increase the power density. Therefore, it is necessary to increase the air supply pressure for PEMFC. But at the same time, many auxiliary devices are appended to the system to provide high-pressure air, such as air compressor, intercooler, and back pressure valve, which make the control of the entire air supply system very complicated. So an excellent control algorithm is needed.

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.

In recent years, with the development of computing infrastructure and methods, the potential of numerical methods to reasonably predict aerodynamic noise in turbocharger compressors of heavy-duty diesel engines has increased. However, aerodynamic acoustic modeling of complex geometries and flow systems is currently immature, mainly due to the greater challenges in accurately characterizing turbulent viscous flows. Therefore, recent advances in aerodynamic noise calculations for automotive turbocharger compressors were reviewed and a quantitative study of the effects for turbulence models (Shear-Stress Transport (SST) and Detached Eddy Simulation (DES)) and time-steps (2° and 4°) in numerical simulations on the performance and acoustic prediction of a compressor under various conditions were investigated.