To make full use of the discrete characteristics of electronic semiconductor switches to establish domination set, finite set model predictive was applied to bidirectional chargers. The predictive model of LCL filter was built up, and based on this model, current of the next control cycle can be calculated, and a cost function was built to achieve vector optimization. The steady-state and dynamic-state performance of the bidirectional charger were tested by simulation and experiment, which show that grid-side current features high sinusoidal output and fast response, verifying correctness and feasibility of the control strategy.

The difficulty in accurately predicting the future driving conditions of new energy vehicles leads to inaccurate estimation of the battery’s State Of Charge (SOC), to address this issue, this paper proposed a real-time online prediction of the future driving conditions of electric vehicles based on digital map real-time traffic information and machine learning algorithms, experimental verification was conducted on an Internet-distributed vehicle-in-the-loop simulation platform. The results show the proposed algorithm has high real-time performance and prediction accuracy.

To improve the handling stability of distributed drive electric vehicles, this paper proposed an integrated control strategy for active front wheel steering and direct yaw torque based on model predictive control and phase plane method. The strategy consists of a vehicle stability judgment module based on the centroid sideslip angle phase plane, a stability controller based on improved model predictive control and a torque divider. Through the evaluation of vehicle stability on the phase plane, the vehicle stability formula was introduced into the objective function design of the model predictive controller, the active angle and additional yaw moment of the decision were adjusted. Considering vehicle stability and dynamics, a torque synthesis optimization allocation method based on phase plane was designed to allocate torque. Simulation under low adhesion and double lane shifting conditions shows that the control strategy improves the handling stability of the vehicle under medium and low adhesion conditions.

In order to track the regenerative braking power of EV’s hybrid energy storage system accurately, avoid current impact of energy storage elements and bus voltage build-up caused by untimely distribution of braking energy, this paper proposed a dynamic power tracking control based on hybrid energy storage system. The supercapacitor was modeled based on the Thevenin model, the terminal voltage of the supercapacitor was predicted with Kalman filter. On this basis, a loss function was established for power tracking, the supercapacitor current was solved in real time under constraints. Finally, the effectiveness of the proposed dynamic power tracking control strategy was verified by contrast test. The test results show that the proposed dynamic power tracking control can quickly track the braking power and reduce the current impact of energy storage elements.

In order to explore the practical effects of Cellular Vehicle-to-Everything (C-V2X) in specific application scenarios, the design and experimentation of in-vehicle traffic light application scenarios was conducted based on C-V2X. A real road environment was constructed for in-vehicle traffic light display, red light warning scenario, road test was conducted at different communication distance and vehicle speed conditions using two communication modes based on Long Term Evolution Vehicle-to-Everything (LTE-V2X) and the 4th Generation mobile communication technology (4G). The experimental results show that both scenarios were effectively triggered, with communication delays of less than 1 s, a packet loss rate of less than 2% and a message accuracy rate of no less than 99%, meeting the basic performance requirements of the scenarios. In addition, LTE-V communication has the advantages of long-distance, low latency and high reliability.

In order to realize the bidirectional flow of energy between electric vehicles and the power grid in Vehicle to Grid (V2G) mode, this paper analyzed the working principle using the non-isolated two-stage bidirectional AC/DC converter as the research object, a mathematical model was established. The feed forward decoupling double closed-loop, constant current charge and discharge control strategies were formulated. The Space Vector Pulse Width Modulation (SVPWM) technology was utilized to generate the driving signal of the power switch tube, the passive damping LCL filter was designed. Finally, the system simulation model was built in MATLAB/Simulink for system simulation and analysis. The results show that the proposed control strategy can realize the bidirectional power flow between the electric vehicle and the power grid.

In order to improve the integration of free piston expander-linear generator, the motion of air in cylinder and the motion characteristics of piston were analyzed by using computational fluid dynamics method and dynamic grid technique, and the influence of cylinder structural parameters on the motion characteristics of piston was analyzed by orthogonal test. The results show that the air pressure and velocity change obviously when the air flows through the inlet and outlet. Cylinder orifice aperture has the greatest influence on piston velocity and displacement. Intake time, piston mass and cylinder diameter also have different influences on piston displacement and velocity.

A motor noise tracking method based on Empirical Mode Decomposition spectral Kurtosis Reconstruction Find Peaks (EMD-KR-FP) was proposed to track the noise of brushless DC motor. Firstly, the characteristic frequencies of radial electromagnetic force, torque ripple and resonance induced electromagnetic noise were obtained by theoretical calculation. The characteristic frequency set was defined, the time-domain signal of motor noise was decomposed by Empirical Mode Decomposition (EMD) method. Then, according to the spectral Kurtosis theory, the Intrinsic Mode Function (IMF) component was screened for signal reconstruction, the reconstructed signal was processed by Fourier transform. Peak location algorithm was used to extract the peak frequency of several peaks with the largest contribution on the spectrum and compare with the frequency of characteristic frequency set, so as to determine the cause of electromagnetic noise of the motor. Test results show that this method is effective and reduces the workload of traditional order analysis method.

For the problem of communication delay caused by the interference of the communication channel in the process of vehicle-road cooperative V2X communication, this paper proposed a V2X delay attack protection technology based on game theory. With the signal-to-noise ratio as the measurement index of communication quality, this paper firstly studied the function relationship between the transmission power of the attacking node and the signal-to-noise ratio to obtain the function relationship between the transmission power of the legitimate node and the attacking node, then this functional relationship was brought into the relationship between transmission power of legitimate node and the signal-to-noise ratio. This function also considered the probability of signal transmission, the probability of being detected and the transmission interference of other nodes to obtain the target function. Finally, the proposed protection technology is verified by simulation and test. The results show that by adjusting the transmission power of the nodes, it can effectively resist the attacker’s interference on the V2X communication channel and reduce the communication delay.

To ensure the security and privacy of sensitive data in Internet of Vehicle (IoV) environments, this paper proposed a distributed differential privacy data protection scheme combining federated learning and reinforced learning mechanisms. In this scheme, a federated learning architecture was applied to keep data on vehicle nodes or edge devices for learning, enabling data privacy protection, reducing data transmission costs through distributed storage. The Laplace mechanism was employed to achieve differential privacy, the Layer-wise Relevance Propagation (LRP) was used to manage data perturbation, ensuring the privacy and efficiency of model parameter transmissions. Experimental results show that the proposed scheme can achieve approximately 80% global accuracy within 10 rounds of communication, with a maximum of 98%, can complete model aggregation within less communication rounds, achieving a good balance between privacy protection and global data accuracy, and accurately detecting the injection of false noise through the reinforced learning strategy, promoting the intelligence and security levels of IoV.