植入轮胎的RFID标签天线性能预测研究

发布时间：2018-07-09 10:13

本文选题：RFID电子标签 + 智能轮胎　；参考：《青岛科技大学》2017年硕士论文

【摘要】：轮胎是汽车最重要部件之一,其性能和状态直接决定汽车运行安全。智能轮胎通过植入RFID电子标签,可将轮胎信息传至车辆控制系统,并传输至云服务器,车主可实时获取信息,保证车辆运行安全。并且,轮胎中的RFID电子标签就像每个人都有唯一的身份证,轮胎有了自己的“身份证”,随时可以通过采集终端读取相应数据,结合相应的管理软件,从而实现对轮胎全生命周期数据的记录及追溯。RFID电子标签在空气中的阅读距离可以达到很大的距离,但是一旦植入轮胎中,很容易受到轮胎中的金属层和炭黑等电介质的影响,导致读取距离下降。所以,需要寻找合适的方法来预测RFID电子标签在不同的轮胎环境下阅读器的读取距离。本文对植入轮胎中RFID标签的螺旋弹簧式天线进行神经网络的建模预测。首先,利用电磁仿真软件FEKO仿真不同天线臂长的标签植入轮胎后在不同轮胎介电参数、植入轮胎深度、与钢丝层距离情况下的辐射特性和回波损耗。然后,根据仿真软件仿真结果对不同天线单臂长、轮胎介电参数、植入轮胎深度、与钢丝层距离等进行分析取值。作为BP神经网络的输入训练组。第三,实验设计。按照上述输入训练组中每组的参数设置,将不同单臂长的标签植入不同轮胎环境中,使用Alion ALH-9000阅读器依次得到阅读距离数值(实验条件限制,可能会存在一定误差),作为BP神经网络的输出训练数据。最后,训练BP神经网络,误差在允许范围内后,可以使用此模型作为植入轮胎的RFID标签天线的预测模型。因此,可以通过建立BP神经网络模型的方法,快速方便的在一定精度范围内预测阅读器的阅读距离,优化植入轮胎中的RFID电子标签的性能。
[Abstract]:Tire is one of the most important parts of automobile, and its performance and status directly determine the safety of vehicle operation. Intelligent tire can transmit tire information to vehicle control system and cloud server by inserting RFID tag. The owner can obtain information in real time to ensure the safety of vehicle operation. Moreover, the RFID tag in the tire is like everyone has a unique ID card. The tire has its own "ID card" and can read the corresponding data through the collection terminal at any time, combining the corresponding management software. In order to record and trace the whole life cycle data of the tire, the reading distance of RFID tag in the air can reach a very big distance, but once it is implanted in the tire, it is vulnerable to the influence of the metal layer and carbon black in the tire. Causes the read distance to drop. Therefore, it is necessary to find a suitable way to predict the reading distance of RFID tags in different tire environments. In this paper, the neural network modeling and prediction of the helical spring antenna implanted with RFID tag in tire is carried out. First, the electromagnetic simulation software FEKO is used to simulate the radiation characteristics and echo loss of different tire dielectric parameters, tire depth, distance from steel wire layer after the labels with different antenna arm lengths are implanted into the tire. Then, according to the simulation results, the different antenna single arm length, tire dielectric parameters, the depth of the implanted tire, and the distance from the wire layer are analyzed. Third, experimental design. According to the parameters of each group in the input training group, the label with different arm length was implanted into different tire environment. The reading distance value was obtained by using Alion ALH-9000 reader. There may be a certain error), as the BP neural network output training data. Finally, BP neural network is trained. When the error is within the allowable range, this model can be used as the prediction model of RFID tag antenna implanted in tire. Therefore, the BP neural network model can be established to quickly and conveniently predict the reading distance of readers in a certain range of accuracy, and optimize the performance of RFID tags implanted in tires.
【学位授予单位】：青岛科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U463.6;TP391.44

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