Search Results

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.