In order to study the influence of shielding structure on electvical vehicles wireless charging system, based on the theory of shielding effectiveness, this paper establishes a relationship model between the thickness of the shielding material and its parameters. Firstly, the shielding effectiveness of single-layer shielding materials with different thicknesses and different shielding materials on the magnetic field is analyzed, and the numerical solutions are compared with the analytical solutions to preliminarily validate the accuracy of the established shielding material thickness model. Based on this, a composite wireless charging shielding structure with the minimum thickness is proposed under the premise of ensuring electromagnetic safety and verified by electromagnetic tests. The results show that when a combination of 0.05 mm ultra-thin silicon steel and 2.52 mm ferrite is used as a dual-layer shielding, the magnetic field intensity reaches the safety limit, and the transmission efficiency reaches 90.92%. Compared with the traditional ferrite and aluminum composite shielding structure, the thickness of the shielding structure is reduced by 1.95 mm.

In order to suppress the knocking phenomenon of hybrid engine, a Computational Fluid Dynamics (CFD) model is established based on the ultra-high compression ratio hybrid engine. Bench tests are carried out to analyze the influence of engine subsystem and control parameters on knocking. The results show that rapid combustion can be achieved by increasing the inlet tumble ratio. Improving the machining accuracy of the combustion chamber can improve the consistency of combustion. Separate cooling of cylinder block and cylinder head can realize intelligent temperature regulation. The addition of a water baffle can reduce the temperature of the metal. For the maximum effective thermal efficiency point of the engine, reducing the pressure loss of the Exhaust Gas Recirculation (EGR) system can make the EGR rate reach 25%. In high-temperature environments, the effective compression ratio and engine outlet temperature require precise control.

Carbon Fiber Reinforced Plastic (CFRP) were used to replace traditional metals to construct battery pack box to achieve lightweight design of battery pack box. Firstly, based on performance requirements, finite element analysis was conducted for the dynamic and static performance of carbon fiber battery pack, topography and size optimization was carried out on the upper cover plate, and structural optimization was made on the lower box body respectively, which increased the first-order natural frequency to 50.63 Hz and reduced weight of the lower box by 31.1%. Secondly, optimization analysis was made on the box layer, and based on the Isight platform, multi-objective optimization was conducted on the weight and first-order natural frequency of the lower box, meanwhile the entropy TOPSIS decision-making method was used to determine the optimal layer design scheme. Finally, the layer sequence was optimized by considering the lamination board laying process. The optimization analysis results show that lower box achieves a weight reduction of 58.9%, and both the maximum displacement and maximum stress under all operating conditions were reduced, and the dynamic and static performance of the battery pack box has been improved.

In order to solve the problem of the robustness of the performance of automotive acoustic package parts in mass production, an uncertainty optimization method based on interval analysis is proposed. The BIOT theory and the transfer matrix method are used to simulate the sound absorption and insulation performance of the acoustic package parts, the Interval perturbation theory is used to analyze the uncertainty of acoustic performance, and the interval uncertainty optimization method is introduced to optimize the material selection and structural design parameters of the parts. The results show that the method is used to analyze and design the inner front wall parts of a certain model, the quality of the parts decreases by 12.8%, and the robustness of the system is greatly improved, and the maximum fluctuation of insertion loss decreases from 8 dB before optimization to 5 dB after optimization.

To address the cracking issue of the rear rubber bushing in the front suspension lower control arm during road testing for a specific vehicle, a life simulation method considering the influence of the diameter shrinkage is proposed. Firstly, the static stiffness of the bushing after diameters shrinkage is simulated, and combined with multi-objective optimization, hyper elastic constitutive parameters that accurately describe the force-displacement characteristics of the bushing are obtained. Then, on the basis of considering pre-strain from diameter shrinkage, crack grow algorithm methods are employed to predict bushing life, achieving a damage level of 2.01 at critical location, which replicates the cracking problem observed in road tests. Finally, through structural optimization design, damage at hazardous locations is reduced to 0.943. The road test results show that the fatigue life of the optimized bushing can be improued effectively, and the proposed scheme is proved to be effective.

In order to prevent vehicle mass changes and road slope interfering with longitudinal speed of autonomous driving truck, this article utilizes an intelligent navigation system to obtain information including vehicle speed trajectory and road slope. Vehicle longitudinal dynamic model and Compressed Natural Gas (CNG) engine dynamic model are established, and a real-time Dynamic Programming (DP) speed trajectory tracking controller is designed based on the Model Predictive Control (MPC) framework. The simulation results under NEDC and WLTC operating conditions show that the controller can keep vehicle speed stable under conditions of truck mass change and road slope interference, and can optimize speed tracking error while reducing natural gas consumption.

To achieve accurate prediction of EV battery energy information, this paper proposed a method for battery energy analysis and prediction based on big data of chargeable pure electric vehicles. Firstly, the big data of vehicles with the same battery model from different regions were obtained through a big data platform, and then the interval average method and Support Vector Regression (SVR) were used to fit the relationship between mileage and total energy for both the total data and typical regional data, to predict degradation of the battery total energy. Finally, the predicted results were compared with that obtained from Long Short-Term Memory (LSTM) neural network, and the accuracy of the proposed method was verified by vehicle test. The results show that: the SVR-based model can quantitatively fit the degraded battery capacity, which has high prediction accuracy.

In order to improve success rate and accuracy of automatic parking, firstly, the input image features were extracted based on Convolutional Neural Network (CNN) model, and then the encoding-decoding mechanism of Transfomer model was used to tile the image features extracted by CNN for calculation and inference. Finally, the target prediction results were obtained by feedforward neural network. In this paper, fisheye images were used to recognize the target. The center point of the parking angle and the center point of the empty parking entrance were expressed by two-dimensional coordinate points, which reduced the redundancy of the output information and optimized the model structure. The test results show that the algorithm can better adapt to different parking space line marking mode and different natural environment, with the recall rate of target perception reaches 98%, and the average error of parking space corner center location is less than 3 cm, which meets the requirements of real-time application for robustness, real-time and accuracy.

In order to improve the attitude angle solving accuracy of Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) in unmanned vehicle system, this paper proposed a Particle Swarm Optimization (PSO) based algorithm and a Strong Tracking Adaptive Unscented Kalman Filter (STAUKF) data fusion method. Firstly, two kinds of IMU modules with different precision were filtered by STAUKF algorithm. Secondly, two kinds of error functions were constructed and PSO algorithm was introduced to fuse the two kinds of IMU posterior estimation. Finally, the test was carried out on the built unmanned vehicle platform. Experimental results show that, compared with the data solved by two single IMU sensors, the root mean square error of the transverse roller shaft and pitch shaft angle solved by the proposed algorithm is reduced by 56.67% and 58.94%, respectively, and the data solved is reduced by 36.55% and 52.15% respectively compared with direct weighted average of the redundant dual IMU system. Therefore, the algorithm proposed in this paper is more accurate and robust.

In order to improve the energy management of Range Extended Electric Vehicle (REEV), firstly Long Short-Term Memory (LSTM) neural network was used to predicate vehicle speed, then calculates the demand power in the prediction time domain, and the demand power in the prediction time domain and the demand power at the current moment were jointly inputted to the Deep Deterministic Policy Gradient (DDPG) agent, which outputted the control quantity. Finally, the hardware-in-the-loop simulation was carried out to verify the real-time performance of the control strategy. The validation results show that using the proposed LSTM-DDPG energy management strategy reduces the equivalent fuel consumption by 0.613 kg, 0.350 kg, and 0.607 kg compared to the DDPG energy management strategy, the Deep Q-Network (DQN) energy management strategy, and the power-following control strategy, respectively, under the World Transient Vehicle Cycling (WTVC) conditions, which is only 0.128 kg different from that of the dynamic planning control strategy when the dynamic planning control strategy is used.