Research On Performance Prediction Of RFID Tag Antenna For Tire

Tire is one of the most important parts of the car,its performance and status directly determine the safety of the car.Intelligent tires through the implantation of RFID tags,pressure,temperature and wear and other information can be transmitted to the vehicle control system,and transmitted to the cloud server,the owner can access information in real time to ensure the safety of vehicles running.And the RFID tag in the tire is like everyone has a unique ID card,the tire has its own "identity card",at any time through the collection terminal to read the corresponding data,combined with the corresponding management software,in order to achieve the tire Record and trace of life cycle data.RFID electronic tags in the air reading distance can reach a great distance,but once implanted in the tire,it is vulnerable to the metal layer in the tire and carbon black and other dielectric effects,resulting in decreased reading distance.Therefore,you need to find the right way to predict the RFID reader in different tire environments read the reader distance,optimize the performance of the label.In this paper,neural network modeling and prediction of helix spring antenna with RFID tag in tire is carried out.First of all,the use of electromagnetic simulation software FEKO simulation of different antenna arm length of the label after implantation of tires in different tire dielectric parameters,implanted tire depth,and the distance between the wire layer radiation characteristics and return loss.Then,according to the simulation software simulation results for different antenna single arm length,tire dielectric parameters,implanted tire depth,and the distance between the wire layer and so on.As an input training group for BP neural networks.Third,experimental design.The Alion ALH-9000 reader was used to obtain the reading distance values(the experimental conditions were limited,and there may be some errors),according to the parameters set in each group of the input training group,and different labels with different arms were implanted in different tire environments.As the BP neural network output training data.Finally,after training the BP neural network,the error can be used within the allowable range,which can be used as a prediction model for RFID tag antennas for implanted tires.Therefore,the BP neural network model can be used to quickly and easily predict the reading distance of the reader in a certain precision range and optimize the performance of RFID tags in implanted tires.