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.

For the problems of strong randomness and low training efficiency in intelligent vehicle training under reinforcement learning algorithm, this paper proposed a driving decision framework of intelligent vehicle based on rule constraints and Deep Q Network (DQN) algorithm. The introduced rules were divided into hard constraints related to lane change and soft constraints related to lane keeping, which were implemented by Action Detection Module and reward function respectively. At the same time, the network structure of DQN was improved by combining Dueling DQN and Double DQN, N-Step Bootstrapping learning was introduced to accelerate the training efficiency of DQN. Finally, the effectiveness of the model was verified by comprehensive comparison with the original DQN algorithm in the highway scene of Highway-env platform. The improved algorithm improved the task success rate and training efficiency of intelligent vehicles.

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%.

To improve the performance of vehicle detection algorithm based on 3D point clouds, this paper proposed a real-time vehicle target detection algorithm based on the bird’s-eye view of point cloud. First, the original 3D vehicle point cloud was converted into the 2D point cloud RGB feature map. Second, the vehicle’s yaw angle prediction branch was added to achieve accurate vehicle localization based on YOLOv4-tiny network, the target localization capability of the network was improved by adding an improved Spatial Pyramid Pooling-Fast (SPPF) module. Finally, the target detection precision was improved by introducing a dual-attention mechanism and optimizing the loss function in the backbone network. The test results show that the average vehicle detection precision of the proposed algorithm reaches 96.92%, which is 2.94 percentage points higher than YOLOv4-tiny, the average detection accuracy of the proposed algorithm reaches 87.73% in the moderate difficulty of KITTI bird’s-eye view validation set, detection rate reaches 100 frames per second, which is able to meet the real-time requirements.

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.

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.

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.

A solution was proposed to address the issue of low generalization ability of diagnostic models for electric vehicle power battery faults caused by sparse data, which utilized a data augmentation method based on Generative Adversarial Networks (GAN). According to the augmented data, a fault diagnosis scheme was designed using the Random Forest (RF) model combined with the Bayesian Optimization (BO) method to form a GAN-RF-BO battery fault diagnosis framework. The proposed fault diagnosis approach was compared with the common Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT) model on a real fault dataset. The results show that the accuracy of the proposed method is improved by 19.66%, 19.71% and 16.31% compared to the MLP, SVM, and GBDT models respectively. The GAN-RF-BO framework can better utilize sparse data to troubleshoot problems with power batteries.

A battery fault online diagnosis method based on Kalman filtering and feature indexing was proposed for the faults such as thermal runaway and internal short circuit in the battery pack. Firstly, data noise reduction was performed based on historical data and Kalman filtering method to effectively remove voltage anomalies and a feature indexing method was proposed to extract and amplify the voltage characteristics between battery pack cells. Finally, a fault value calculation method based on cosine similarity was proposed in order to reduce the false alarm due to battery pack inconsistency and to automatically detect and locate the faulty battery online. Verification in cloud-based vehicle data shows that the proposed battery fault diagnosis algorithm based on Kalman filtering and feature indexing can effectively detect faulty batteries and provide early warning.

In order to reveal the nail penetration safety characteristics of power lithium-ion batteries for electric vehicles, an experimental platform was built for 18650/21700 power lithium-ion batteries. With the help of the classical internal short circuit model based on the electronic flow direction of the internal and external circuits of the batteries, the effects of associated influencing factors including different State of Charge (SOC), nail speed, nail diameter and battery size on the safety characteristics of battery nail penetration were explored. The results show that the higher the state of charge, the faster the nail speed and the larger the nail diameter and battery size are, the higher the risk of thermal runaway of battery nail penetration is. When the SOC of 18650/21700 power lithium-ion battery is in the range of 40% and 20% respectively, the battery has good nail penetration safety.