基于毫米波雷达的电动汽车无线充电运动异物检测与跟踪

doi:10.19595/j.cnki.1000-6753.tces.211570

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摘要电动汽车无线充电系统的一次线圈和二次线圈之间存在大空间气隙以及高强度磁场,可能对进入充电区域的生物体造成电磁伤害。因此,系统需要配备可靠、灵敏的生物体检测装置,以便在生物体侵入保护区域时及时降低或关闭充电功率。该文研究一种基于77GHz毫米波雷达并结合卡尔曼滤波和数据关联算法的运动异物检测方法,首先通过调频连续波雷达测量运动异物的速度、距离和角度;其次融合卡尔曼滤波、目标聚类和数据关联算法实现多目标运动异物的轨迹跟踪。仿真和实验结果表明,该方法能够高精度地测量运动异物的位置和速度,并具备相邻多目标检测和跟踪能力。

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关键词 ：电动汽车无线充电, 生物体检测, 毫米波雷达, 卡尔曼滤波, 轨迹跟踪

Abstract：In recent years, the electric vehicle wireless charging system (EV-WCS) has been widely concerned because it is safe, reliable, automatic, and environment-friendly. The high-frequency and high-power electromagnetic field between the transmitting coil and receiving coil of an EV-WCS may be harmful to an intruded living object. Therefore, a reliable and sensitive living object detection device has to be equipped to reduce or close the charging power as living objects entering the wireless charging region. Existing studies mainly focused on the presence/absence detection of living objects, but lacked research on multiple object detection and moving trajectory track, which are both benefit for practical applications. This paper develops a 77GHz millimeter-wave radar-based moving foreign object detection method based on Kalman filter and data association algorithms. Simulation and experiments are both implemented to verify the effectiveness of the proposed method.
Firstly, principle of the frequency modulated continuous wave radar is introduced. Formulations for calculating distance, velocity, and angle, are deduced. Secondly, a method that combines a Kalman filter with target clustering and data association algorithms is developed to track the moving trajectories of multiple objects. In particular, the Kalman filter is employed to reduce the influence of measurement noises. The DBSCAN algorithm is used for target clustering. The joint probabilistic data association algorithm is utilized to track multiple object trajectories. Finally, the proposed method is evaluated with simulation and experiments on TI IWR1642 77GHz radar in terms of measurement accuracy and multiple objects track.
The results show that the proposed method can simultaneously measure the distance, velocity, angle, and number of moving objects with an average distance error of 2cm. Particularly, it can distinguish multiple objects that are very close to each other. With the DBSCAN algorithm, the proposed method can cluster five simulated objects with a maximum error of 3cm, and average errors of 0.63cm, 0.66cm, 0.48cm, 0.60cm, and 0.71cm, respectively. In addition, the proposed method is able to track the trajectories of the five objects within 15 calculating cycles, and the average errors are 0.35cm, 0.39cm, 0.39cm, 1.2cm, and 0.92cm, respectively. Also, experimental results indicate that the proposed method can track the moving object with a position error band of 5cm.
Conclusions of the paper can be summarized as follows: ① The proposed method is able to detect the position and velocity of moving foreign objects accurately, and the maximum error for distance measurement is within 2cm. ② The proposed method can realize presence detection and trajectory track of multiple adjacent moving objects. ③ With the capability of moving object track, the proposed method is valuable for improving reliability of living object detection, and realizing more reasonable protection. For instance, the wireless charging system can determine to lower or close the output power by considering the position range and moving trend of objects.

Key words： Electric vehicle wireless charging living object detection millimeter-wave radar Kalman filter trajectory tracking

收稿日期: 2021-10-07

PACS:

TM724

基金资助:广东省重点领域研发计划资助项目（2020B0404030004）

通讯作者: 田劲东男,1973年生,教授,研究方向为光学检测技术及其应用。E-mail: jindt@szu.edu.cn

作者简介: 田勇男,1985年生,博士,副教授,研究方向为无线电能传输技术。E-mail: ytian@szu.edu.cn

引用本文:

田勇, 杨昊, 胡超, 田劲东. 基于毫米波雷达的电动汽车无线充电运动异物检测与跟踪[J]. 电工技术学报, 2023, 38(2): 297-308. Tian Yong, Yang Hao, Hu Chao, Tian Jindong. Moving Foreign Object Detection and Track for Electric Vehicle Wireless Charging Based on Millimeter-Wave Radar. Transactions of China Electrotechnical Society, 2023, 38(2): 297-308.

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