In order to reduce load fluctuations and network losses caused by large-scale electric vehicles connected to the distribution network, this paper proposed a multi-objective optimization control method based on Particle Swarm Optimization (PSO) algorithm for flexible loads of large-scale electric vehicle charging connected to the distribution network. Firstly, a coupling model between transportation network and distribution network was established, and combine it with the travel chain model to analyze users’ charging needs, and a prediction model for the energy state of connected electric vehicles was established; Secondly, the minimized standard deviation of load fluctuations and network losses in the distribution network was taken as the optimization objective, and a multi-objective optimization function was established for the flexible load integration of large-scale charging of electric vehicles into the distribution network, meanwhile distribution entropy was introduced to design inertia weight update strategy and optimize PSO algorithm. Finally, the improved PSO algorithm was used to achieve flexible load control of the distribution network based on functional constraints. The test results show that the proposed method can accurately analyze the charging needs of users, and reduce the peak load fluctuation and network loss of the controlled distribution network.

To improve the prediction accuracy of electric vehicle charging load, a multi time scale prediction model for electric vehicle charging load was designed based on the Lightweight Gradient Boosting Machine (LightGBM) algorithm and travel chain theory. The travel chain was used to describe the user’s travel process, Monte Carlo method was used to extract the spatiotemporal data, and the probability density functions of travel and stay time in different regions was calculated. Newton method was used to divide the probability of charging at multiple time scales, clarifying the spatiotemporal distribution of driving and charging conditions. Fuzzy mathematics theorem and LightGBM were applied to classify charging load data, and a multi season and multi time prediction model were constructed. The efficient parallel computing mode of LightGBM was applied which clarified the variation pattern of charging load, and multi time scale prediction was achieved. The experimental results show that the established model has a prediction error of less than 100 kW and a prediction false alarm rate of less than 3% under different seasons and the number of electric vehicles, and can accurately display the variation pattern of charging load.

In order to accurately predict the V2G charging and discharging load of electric vehicles, so as to regulate the peak to valley difference of power grid load and ensure power supply stability, this paper proposed a spatiotemporal distribution prediction method for V2G charging and discharging loads of electric vehicles based on collaborative optimization of supply and demand sides. A collaborative optimization objective model for both supply and demand sides was built, the Whale Optimization Algorithm was used for iterative solution to obtain the optimal charging and discharging load curve, and the optimal charging and discharging period was determined. The influencing indicators of charging and discharging loads within the optimal time periods in different spatial regions were collected, serving as inputs for constructing a prediction model based on multiple linear regression, thus achieving the prediction of spatial-temporal distribution of electric vehicle V2G charging and discharging loads. The experimental results show that the load prediction model obtained with the proposed method has a relatively large coefficient of determination, indicating that the prediction results of this research method are closer to the actual load, and have high prediction accuracy.

Aiming at the problems that the Rapid-exploration Random Tree (RRT) algorithm is easy to lead to node expansion redundancy and the generated path does not meet the vehicle rotation angle constraint, an improved secondary rotation angle constraint RRT algorithm is proposed. Based on the traditional RRT algorithm, the sampling space is first cut, and the target offset strategy is introduced to reduce the sampling time. Then, the vehicle expansion processing and the straight line method are used to detect the obstacle collision, and the first rotation angle constraint is introduced to obtain the coarse solution path. Then, the secondary angle constraint optimization processing is established for the rough solution path, and the vehicle optimization path is obtained and fitted, and the simulation verification is carried out. The results show that compared with the biased RRT algorithm with the goal-oriented strategy, the maximum curvature of the path is reduced by 34.33 %, the average curvature is reduced by 47.36 %, the number of extended nodes is reduced by 47.62 %, the path distance is reduced by 7.76 %, and the planning time is reduced by 14.98 %.

To maximize energy absorption of EV body structure in frontal collision, vehicle front-end structure is designed by analyzing the characteristics of the optimal collision waveform configuration, and combining theoretical and empirical formulas. Meanwhile, deformation modes such as bending are introduced, and a method to improve vehicle structure frontal collision property based on the theoretical optimal waveform configuration is proposed. The results show that the collision waveform of the improved vehicle body structure is basically consistent with the optimal waveform configuration, and there is a significant decrease in passenger acceleration, which improves the overall safety of the vehicle.

Combining spare wheel identification and wheel speed compensation, limited slip feedforward control based on equal utilizable friction coefficient on the front and the rear axle and limited slip feedback control based on axle speed difference were designed. And an on-demand 4WD vehicle dynamics simulation platform was built to verify the effectiveness of small spare tire identification, wheel speed compensation, and limited slip control. The strategy can achieve the central differential and differential slip limiting functions of on-demand 4WD vehicle, improve vehicle’s power performance and driving stability, avoid the error trigger of limited slip control caused by the difference in rolling radius, and solve the abnormal wear and ablation of the multiple-plate clutch caused by the small rolling radius of the spare tire.

In order to address the issue of long cycle, high cost and low safety of traditional vehicle chassis tuning test, this paper proposes a test method of virtual chassis tuning using dynamic driving simulator. By comparing the subjective and objective test results of vehicle dynamics between virtual chassis tuning and real vehicle chassis tuning, it is shown that the virtual chassis tuning of dynamic driving simulator can achieve the same effect compared with the traditional real chassis tuning.

In order to reduce the energy consumption of heat pump air conditioning systems, take the heat pump air conditioning system matching Active Grille Shutter (AGS) thermal management system of electric vehicles as the research object, a heat pump air conditioning thermal management system and energy consumption control strategy are developed based on various parameters including vehicle speed and intake grille opening area. The energy consumption values of the system are simulated and calculated for different AGS openings in high and low temperature environments. Using the calibration method in the actual vehicle cabin environment, this paper analyzes the variations in measured values of air conditioning systems and wind resistance energy consumption for AGS openings in low temperatures of -7 ℃ and high temperatures of 35 ℃. The experimental results show that in high and low temperature environments, the larger the AGS opening, the higher the wind resistance energy consumption value, whereas the lower the comprehensive energy consumption value of air conditioning and wind resistance. When the AGS opening increases by the same value, the impact on energy consumption at high temperatures is greater than that at low temperatures.

The results of whiplash test show that whiplash injury is very serious and whiplash score is low. To solve this problem, the seat finite element model was established by HyperMesh software, and the whipping simulation analysis was carried out. The factors affecting substantially whipping injury were analyzed as follows: angle adjuster stiffness coefficient, head pillow rod diameter, thickness of backrest left support plate, thickness of backrest right support plate, thickness of backrest rear support plate, thickness of seat cushion left support plate and thickness of seat cushion right support plate. The above seven influencing factors were taken as design variables, sample points were collected by Hammersley experimental design, and approximate model fitting was carried out by moving least square method. The global response surface method was used to perform multi-objective optimization of the approximate model. The verification results show that the accuracy of the optimized model meets the requirements, and the whip score was increased, which greatly enhanced the seat’s ability to prevent whip injury.