Volume 24 Issue 4
Aug.  2024
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ZHOU Xi-wei, SHI Wen-shuai, WANG Hui-feng, DAI Liang, WU Qi-sheng, BAI Ye-hong. Typical feature recognition of dynamic anti-migration for wireless charging vehicles in road traffic systems[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 195-207. doi: 10.19818/j.cnki.1671-1637.2024.04.015
Citation: ZHOU Xi-wei, SHI Wen-shuai, WANG Hui-feng, DAI Liang, WU Qi-sheng, BAI Ye-hong. Typical feature recognition of dynamic anti-migration for wireless charging vehicles in road traffic systems[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 195-207. doi: 10.19818/j.cnki.1671-1637.2024.04.015
shu

Typical feature recognition of dynamic anti-migration for wireless charging vehicles in road traffic systems

doi: 10.19818/j.cnki.1671-1637.2024.04.015

  • School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2021YFB2601401

Key Research and Development Program of Shaanxi Province 2022GY-308

More Information
  • Author Bio:

    ZHOU Xi-wei(1975-), male, professor, PhD, zhouxiwei@chd.edu.cn

    WANG Hui-feng(1976-), male, professor, PhD, hfwang@chd.edu.cn

  • Received Date: 2024-03-27
  • Publish Date: 2024-08-28
    Abstract
  • In view of the dynamic feature recognition and classification problem of wireless charging vehicles, a composite induction device based on vehicle-road cooperation with a hybrid electromagnetic induction unit and a geomagnetic field induction unit was designed. In the hybrid electromagnetic induction unit, to realize the induction recognition under dynamic conditions, a detection method with the effective value of resonant current as the feature of wireless charging vehicles was put forward. Two forms of electric field coupling and magnetic field coupling were introduced into the circuit topology, alongside a high-order bilateral capacitor-inductor compensation structure. Additionally, to quantify the coupling degree in semi-open field scenarios, the power ratio parameters between coil transmission and plate transmission were defined, thus realizing the induction recognition under migration conditions. Geomagnetic field disturbance signals captured by geomagnetic sensors during different vehicles passage were taken as an example, and an ensemble empirical mode decomposition (EEMD) method for geomagnetic signals was applied to enhance the extraction effect of nonlinear and non-stationary signals. Furthermore, by introducing a feature vector extraction method for curves and taking small three-box sedans, medium two-box sedans, medium van trucks, and large carriages as typical test samples, the geomagnetic curve signals of various vehicles were transformed into feature vector spectra to determine the vehicle shape types. Research results show that under test conditions, the recognition length of the hybrid electromagnetic induction unit in the direction of coil migration is about 220 mm, the recognition length perpendicular to the direction of outward coil migration is about 170 mm, and the recognition range increases by approximately 62.8% compared to the single magnetic field coupling. Meanwhile, the geomagnetic field induction unit can detect the characteristics of vehicles with lengths ranging from 3.7 to 12.0 m and speeds ranging from 2.78 to 16.67 m·s-1. The reliability of dynamic classification and recognition for wireless charging vehicles can be effectively enhanced by the synergistic cooperation between the geomagnetic field induction unit and the hybrid electromagnetic induction unit, thus promoting the application and development of wireless charging technology in road traffic electrification facilities.

     

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