In order to improve the heat exchange efficiency of the oil passages and windings of oil-cooled motors, continuously spray oil to key positions of the motor, this paper, based on a high-voltage oil-cooled flat wire motor, used fluid mechanics and heat transfer theories as well as computer fluid mechanics (CFD) simulation tool for numerical simulation. The paper firstly conducted flow distribution simulation and estimation, then made local simulation, to analyze the cooling state of motor stator, then explored the principle, effect, advantages and disadvantages of the winding spray cooling method by quantifying the oil flow state.

In order to improve the communication efficiency and security of Vehicular Ad-hoc NETwork (VANET), this paper proposed an anonymous identity authentication and group key distribution scheme based on quantum key and blockchain. Anonymous credentials for vehicles were generated by a combination of random numbers on the vehicle side and random numbers in the cloud, which achieved privacy protection for the vehicle during authentication. The utilization of blockchain for secure distribution of group keys has been proposed, which reduced the computational overhead of the Quantum Secret Service platform and enabled vehicle revocation and traceability. A two-stage key generation method was devised to ensure the security and efficiency of group key distribution in various scenarios, as well as achieve forward and backward security. The signaling and computation overheads were calculated, the signaling overhead was reduced by nearly half. During the group key distribution process, the computational overhead at the vehicle was reduced by 44%, while the computational overhead at the roadside is approximately 20% of the overhead in the comparison scheme. The formal security analysis results proved the security and feasibility of this scheme.

In order to solve the electromagnetic noise issues of Permanent Magnet Synchronous Motor (PMSM) on Electric Vehicle (EV), this paper, based on its electromagnetic noise generation mechanism, proposed a way of reducing electromagnetic radial force by means of test verification to optimize its electromagnetic noise. From the 2 aspects of structural hardware and control strategy, the optimization and improvement were carried out by the verification and comparison approaches such as rotor segmented skew pole optimization, structural stiffness optimization and coupling resonance improvement, current harmonic injection and gap flux density optimization, and each optimization approach was illustrated with practical development case. The result shows that each method has an attenuation of at least 3 dB(A) on electromagnetic noise of PMSM, which further demonstrates the effect and advantage of improving electromagnetic noise by optimizing electromagnetic radial force though experimental means.

To realize secure transmission of vehicular network related data and privacy protection, this article proposed a quantum key based identity authentication and data access control scheme for the vehicular networks. An identity authentication scheme and key agreement mechanism based on pre-charge quantum keys were designed, vehicle data access control scheme based on quantum random number generator was proposed to generate quantum encryption keys, allowing the vehicle owner to control access requests for vehicle networking data from external devices to prevent unauthorized access, malicious intrusion by high-privileged personnel and improper opening of vehicle privacy data. Finally, this article conducted security and performance analysis, analysis results show that this scheme has good security, with a computational cost of 0.395 ms and a communication cost of 420 B, which are lower than that of other schemes.

A hierarchical control strategy based on Radial Basis Function Neural Network (RBFNN) and Stochastic Dynamic Programming (SDP) was proposed for Plug-in Hybrid Electric Vehicle (PHEV) queue. Firstly, the powertrain structure and mathematical model of PHEV were analyzed in detail, then a hierarchical control framework was constructed. The upper layer adopted RBFNN to train driving data derived from Model Predictive Control (MPC) to generate the speed tracking controller. According to the information of the speed and power demand transmitted from the upper layer, a Markov chain model was established for the lower layer controller, the Markov chain model can realize the optimal energy distribution between PHEV traction battery and the engine based on the SDP theory. The simulation results show that compared with CD/CS strategy and rule-based strategy, the energy consumption of PHEVs in the queue is significantly reduced while ensuring safe driving under high-speed conditions based on the proposed strategy.

In order to improve vehicle aerodynamic coefficients comprehensively, this paper proposed a shape optimization design scheme. Firstly, vehicle without crosswind was simulated numerically by using realizable k-ε turbulence model. The reliability of the simulation model was verified by wind tunnel tests. On this basis, the influence of different crosswind angles on the aerodynamic characteristics was studied, the aerodynamic coefficients of yaw angle of 12° were taken as the reference benchmark for optimization, samples were extracted by uniform Latin hypercube for flow field calculation, the response surface model was used to approximate the corresponding relationship between automobile modeling parameters and aerodynamic coefficients, the Pareto front solutions were obtained based on the genetic optimization algorithm. Finally, 4 optimization schemes were determined, which reduced the drag coefficient by 2.6%, the lateral force coefficient by 6.54%, and the lift coefficient tends to be negative, effectively improving the aerodynamic characteristics of the vehicle.

In order to improve the fuel economy of Plug-in Hybrid Electric Vehicle (PHEV), this paper proposed the vehicle speed prediction method based on real-time traffic information. The energy management strategy was established to obtain the optimal fuel economy based on Model Prediction Control (MPC), the real-time optimal torque distribution was optimized with the help of dynamic programming algorithm. Simulation verification was conducted on MATLAB/Simulink platforms which showed that the accuracy was improved by 13.5% compared with traditional vehicle speed prediction methods in real-time road conditions. Compared with the MPC strategy based on historical vehicle speed, the fuel economy of MPC strategy based on real-time traffic information is improved by 9.5%.

For the energy management of new energy vehicles, it is difficult to predict the vehicle speed in a long-term and accurate way, this paper proposed a model-based parametric prediction method to predict the vehicle speed trajectory using the forward-looking data provided by sensors and GPS. Firstly, the speed prediction algorithm based on Intelligent Driver Model (IDM) was established in term of vehicle dynamics and vehicle stop-turn trend; secondly, data was selected from the NGSIM public data set for parameter calibration and simulation; then the algorithm parameters were calibrated using Genetic Algorithm (GA). The results show that the optimized speed prediction algorithm has high accuracy for long-term speed prediction in both unobstructed and congested traffic environments, the error can be controlled in the range of 8%~13%.

In order to accurately evaluate the dark current of pure electric bus and avoid vehicle failure to start due to insufficient battery power, 2 models of pure electric buses were selected, the power supply mode and vehicle dark current of their on-board electrical equipment were analyzed systematically. At the same time, on the basis of summarizing the conventional dark current measurement methods, the long-scale measurement method was proposed, using the electric power tester and measurement module to build a detection scheme to record the dark current and complete the real vehicle test. The results of theoretical analysis and experimental data comparison show that the method based on long scale measurement can accurately evaluate the dark current value of pure electric bus and control it in a reasonable range, then guide the calculation of vehicle outage time.

Generative Artificial Intelligence (Generative AI), a technology based on neural network models for content generation, has received widespread attention in the industry and academia in recent years. With the continuous deepening of the application of this technology in various fields, the large model based on Generative AI has also a huge impact on the transformation of technical solutions in the field of autonomous driving. This article provided a brief overview of the development of Generative AI technology and large models, including their classification methods and representative models. At the same time, this article also delved into the application of generative models for the field of autonomous driving. Finally, a summary and prospect were conducted in the future development direction of Generative AI technology and autonomous driving technology.