In recent few years, the antenna and sensor communities have witnessed a considerable integration of radio frequency identification (RFID) tag antennas and sensors because of the impetus provided by internet of things (IoT) and cyber-physical systems (CPS). Such types of sensor can find potential applications in structural health monitoring (SHM) because of their passive, wireless, simple, compact size, and multimodal nature, particular in large scale infrastructures during their lifecycle. The big data from these ubiquitous sensors are expected to generate a big impact for intelligent monitoring. A remarkable number of scientific papers demonstrate the possibility that objects can be remotely tracked and intelligently monitored for their physical/chemical/mechanical properties and environment conditions. Most of the work focuses on antenna design, and significant information has been generated to demonstrate feasibilities. Further information is needed to gain deep understanding of the passive RFID antenna sensor systems in order to make them reliable and practical. Nevertheless, this information is scattered over much literature. This paper is to comprehensively summarize and clearly highlight the challenges and state-of-the-art methods of passive RFID antenna sensors and systems in terms of sensing and communication from system point of view. Future trends are also discussed. The future research and development in UK are suggested as well.

Chipped radio-frequency identification (RFID) sensor systems have been studied for structural health monitoring (SHM) applications. However, the use of chip in sensor tags and its standardized narrowband operation contribute shortcomings in cost, durability, and detection capability. This paper presents a novel use of the frequency signature-based chipless RFID for metal crack detection and characterization operating in ultra-wideband frequency. The vision is to implement a low-cost and high-temperature-resistant passive wireless sensor able to monitor the crack on a metallic structure with multiparameter detection. We propose a chipless RFID sensor tag integrating four tip-loaded dipole resonators as a 4-bit ID encoder and a circular microstrip patch antenna (CMPA) resonator as a crack sensor. The radar cross section spectrum of the chipless RFID sensor tag generates four resonant frequencies from the dipole resonators and a resonant frequency from the CMPA resonator. Simulation and experimental results show that the resonant frequency shift of the CMPA is a useful feature to indicate the crack orientation and the crack width on a metallic structure. The direction of the resonant frequency shift represents the orientation of the crack, while the magnitude of the resonant frequency shift is proportional to the width of the crack. Furthermore, the experimentation with a natural fatigue crack sample proves that the proposed sensor tag is capable of detecting submillimeter cracks.

One of the challenges in structural health monitoring (SHM) is that defects such as metal cracks and corrosions can occur with multiple material properties, multilayer structure, entangled with influences from the reader system and the environment. This work presents a multiresonance chipless RFID sensor tag and the chipless RFID sensor reader applying principal component analysis (PCA) for multiparameter estimation in metal defect characterization. The proposed sensor tag is composed of crossed diagonal dipole patches and L-shaped patches generating six resonance peaks within 2 to 6 GHz band. The frequency signature of the sensor tag is acquired and processed by a chipless RFID reader implemented using an ultra-wideband impulse radar (UWB-IR) transceiver module. We demonstrate that the multiresonance chipless RFID sensor tag in conjunction with PCA-based feature extraction and selection can be applied for characterization of metal crack with different widths and orientations. Furthermore, the proposed technique can find a robust feature indicating corrosion stages under a coating layer regardless of the sample orientation. This work paves the way for multiparameter defect sensing and monitoring using a chipless RFID sensor system.

Chipless RFID sensors attract attention to structural health monitoring (SHM) because of its advantages of being low-cost, wireless, passive, and having multiple resonances for sensing. Its application for corrosion sensing, however, receives little attention and faces challenges in terms of sensitivity and reliability. This paper proposes a chipless RFID sensor for corrosion characterization based on frequency selective surface (FSS) and feature fusion. An FSS pattern on a substrate is designed to generate three resonances within 2–6 GHz. The ability of the FSS to characterize corrosion thickness was simulated and validated in the experiments. The experimental results using dedicated corrosion undercoating samples show that the FSS based chipless RFID sensor can be used to characterize corrosion, where the three resonance frequency features provide sensitivity and consistent monotonic relations to the corrosion progression. Furthermore, feature fusion using simple sum and confidence weighted averaging (CWA) can enhance the sensitivity and reliability of the sensor. With the low-profile and printability of the sensor, this work paves the way for smart coatings for corrosion sensing and monitoring on metallic structures.

Lithium-Ion (Li-ion) battery is essential in today's energy systems and electric vehicles (EVs). Although Li-ion battery can be charged quickly and have a high energy density, it has several drawbacks, including the rapid degradation of battery performance, especially in terms of battery capacity. Therefore, evaluating its performance degradation is necessary to understand its characteristics. In this paper, the performance degradation of a Li-ion battery is monitored and evaluated from multiple SoC measurements. A simple and low-cost experimental setup consisting of sensors, a microcontroller, and a PC is developed to measure and record the real-time data of Li-ion battery voltage and current. Then, the battery state of charge (SoC) is determined using the Coulomb Counting method, which is based on the incoming and outgoing currents of the battery. As a result, this study derives three parameters that indicate the performance degradation of a Li-ion battery, i.e., SoC, battery capacity, and discharge time. From multiple direct measurements with constant load and C20 discharge process, the minimum SoC value increases from 11% to 18%, while battery capacity decreases from 8.8Ah to 8.3 Ah and, discharge time decreases from 16.9 hours to 16.4 hours. All of those parameters indicate a degradation of around 7% in battery performance. Therefore, this research paves the way for finding a solution to mitigate the quick performance degradation of Li-ion batteries.