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Due to the high center of gravity of medium-duty vehicles, rollover accidents can easily occur during high-speed cornering and lane changes. In order to prevent the deformation of the body structure, which would restrict the survival space and cause compression injuries to occupants, it is necessary to investigate methods for mitigating these incidents. This paper establishes a numerical model of right-side rollover for a commercial medium-duty vehicle in accordance with ECE R66 regulations, and the accuracy of the model is verified by experiment. According to the results, the material and size parameters of the key components of the right side pillar are selected as design variables. The response result matrix was constructed using the orthogonal design method for total mass, energy absorption, maximum collision acceleration, and minimum distance from the survival space.

Traditional vector control needs the installation of mechanical sensors to gather rotor position and speed information in order to enhance the control performance and dynamic quality of electric vehicle wheel motors, which increases system cost and reduces system reliability and stability. On the basis of Popov's super-stability theory, an appropriate adjustable model and reference model are constructed, and the system's reference adaptive law is determined. Furthermore, to solve the problem of the standard PI regulator's poor anti-interference capabilities in speed controllers, the approach of utilizing a sliding-mode speed controller in the speed loop is presented. Finally, a MATLAB/SIMULINK simulation model is created to simulate the motor in three scenarios: no-load start, abrupt speed change, and sudden load change, and a permanent magnet synchronous motor experimental platform is created to validate the control approach.

In order to reflect the actual power consumption of logistics electric vehicles in a city, sample real vehicle road data. After preprocessing, the short-stroke analysis method is used to divide it into working blocks of no less than 20 seconds. Based on principal component analysis, three of the 12 characteristic parameters were selected as the most expressive. K-means clustering algorithm is adopted to obtain the proportions of various short strokes, according to the proportion, select the short stroke with small deviation degree to combine, and construct the driving cycle, it has the characteristics of low average speed, high idle speed ratio and short driving distance. AVL-cruise software builds the vehicle model and runs the driving cycle of urban logistic EV. Compared with WLTC, the difference in power consumption is 34.3%, which is closer to the actual power consumption, the areas with the highest motor speed utilization are concentrated only in the idle area.

The front end structure is an important role in protecting the vehicle and passengers from harm during the collision. Increasing its protective capacity can be achieved by increasing the thickness or replacing high-strength materials. Most of the current research is analyzed separately from these two aspects. This paper proposes a multi-objective optimization method based on weighting factor analysis, which combines material and thickness selection. Firstly, the optimized components are determined based on the 100% frontal collision simulation results. Secondly, six thicknesses and two materials of the front part of the vehicle body are selected as design variables to construct an orthogonal test design. In this paper, a weight-based multi-factor optimization method is used to numerically analyze the response results obtained by orthogonal experiments. Analyze the impact of each factor on the optimization goal to select the most reliable optimization.

As is known to all, the modeling of vehicles and durability simulation is becoming more accurate and more compatible with physical testing, resulting in shortening of the analysis process, and a lower cost. It would be more advantageous in the future to simulate the full vehicle system before the physical testing. Thus, in the analysis of vehicle durability performance, the need for more precise rigid and flexible vehicle modeling and more precise external loadings acquisition method is increasing. In view of the typical difficulties faced in the vehicle multi-body dynamics (MBD) simulation and in the associated loading extraction, this paper proposes a method to achieve accurate external vehicle loadings by virtual simulation. This method is performed based on the physical testing and compensates for the imperfections in the MBD modeling, thus being able to improve the quality of fatigue life prediction (FLP).