Electric vehicles use wireless energy transmission to obtain energy, which can effectively avoid the shortcomings of traditional methods. As the carrier of radio energy transmission and reception, the high temperature of the coil triggers the degradation of wireless transmission performance and the aging of the coil, which may cause fire and other safety problems in serious cases. This paper studied the temperature distribution of the magnetically coupled coil model for electric vehicles. Based on the study of the basic law of heat transfer, the coil model was established using ANSYS software, and the boundary conditions and relevant parameters were set. After many simulation experiments and comparisons, it was finally determined that the transmitting coil and the receiving coil were the same sizes, the inner diameter of the coil was 100 mm, the outer diameter of the coil was 181 mm, and the coupling distance between the transmitting coil and the receiving coil was set to 60 mm. Coil models were simulated and analyzed using different materials. The simulation results show that after 30 min of system operation, the material chosen from the temperature range may have been gold, silver, copper, or aluminum, but from the comprehensive consideration of cost and performance, the material of the coil in the model was finally set to copper. Copper was the best material; its temperature maximum was 74.952 °C and lower than the safety value of 80 °C. It is hoped that this study will provide a reference for wireless charging coil design.

To overcome the limitations of the existing non-contact detection methods of sugarcane nodes, a dynamic detection method was developed based on linear array charge coupled device (CCD) photoelectric signal processing, with the goal of reducing the cost of mechanized sugarcane production and enhancing the efficiency of automatic pre-cut seeds production. First, control the mechanical drive unit to drive a photoelectric detection system to acquire signals of the vertical projection of the sugarcane profile. Then, use the Otsu algorithm for binarization to transform the projection information into profile information. Afterward, the profile signal was decomposed by a variational mode decomposition algorithm that has been optimized based on the sparrow search algorithm, and the component representing the node region was seen as the feature signal. Finally, in the normalized feature signals, the position whose wave peaks were higher than the judgment threshold was considered the position of sugarcane node. Also, one-factor and two-factor experiments were conducted to determine the effects of scanning speed and illuminance on identification accuracy. The results showed that when the scan speed of was 75 mm/s and illuminance was 91.91 lx, the identification rate, average response time and average error value reached 98.42%, 0.13 s and 1.36 mm, respectively. Compared to other node identification methods mentioned in this paper, the method proposed in the paper has a high identification rate and accuracy with faster response, which can enhance the efficiency of sugarcane seed production automation.