Performance Assessment and Comparison of Two Piezoelectric Energy Harvesters Developed for Pavement Application: Case Study

To advance the development of piezoelectric energy harvesters, this study designed and manufactured bridge-unit-based and pile-unit-based piezoelectric devices. An indoor material testing system and accelerated pavement test equipment were used to test the electrical performance, mechanical performance, and electromechanical coupling performance of the devices. The results showed that the elastic modulus of the pile structure device was relatively higher than that of the bridge structure device. However, the elastic modulus of the two devices should be improved to avoid attenuation in the service performance and fatigue life caused by the stiffness difference. Furthermore, the electromechanical conversion coefficients of the two devices were smaller than 10% and insensitive to the load magnitude and load frequency. Moreover, the two devices can harvest 3.4 mW and 2.6 mW under the wheel load simulated by the one-third scale model mobile load simulator, thus meeting the supply requirements of low-power sensors. The elastic modulus, electromechanical conversion coefficients, and electric performance of the pile structure device were more reliable than those of the bridge structure device, indicating a better application prospect in road engineering.

A Fault Diagnosis Design Based on Deep Learning Approach for Electric Vehicle Applications

Diagnosing faults in electric vehicles (EVs) is a great challenge. The purpose of this paper is to demonstrate the detection of faults in an electromechanical conversion chain for conventional or autonomous EVs. The information and data coming from different sensors make it possible for EVs to recover a series of information including currents, voltages, speeds, and so on. This information is processed to detect any faults in the electromechanical conversion chain. The novelty of this study is to develop an architecture for a fault diagnosis model by means of the feature extraction technique. In this regard, the long short-term memory (LSTM) approach for the fault diagnosis is proposed. This approach has been tested for an EV prototype in practice, is superior in accuracy over other fault diagnosis techniques, and is based on machine learning. An EV in an urban context is modeled, and then the fault diagnosis approach is applied based on deep learning architectures. The EV and the fault diagnosis model is simulated in Matlab software. It is also revealed how deep learning contributes to the fault diagnosis of EVs. The simulation and practical results confirm that higher accuracy in the fault diagnosis is obtained by applying the LSTM.

OPTIMIZATION OF A PIEZOELECTRIC GENERATOR OF FLEXURAL VIBRATIONS WITH AN ACTIVE PIEZOMAGNETIC LAYER BASED ON A COMBINATION OF THE FINITE ELEMENT METHOD AND A GENETIC ALGORITHM

The construction of the piezoelectric generator (PEG) for the energy storage device has a significant effect on the electromechanical conversion characteristics, and the optimal choice of its geometric parameters is an important prerequisite for ensuring efficiency of the electromechanical conversion. This article discusses a PEG model based on circular three-layer plate in the ANSYS package. Two piezoactive layers, piezoelectric and piezomagnetic, are glued to a steel substrate. The program built in the MATLAB package allows to control ANSYS scripts to find the dependence of the electromechanical coupling coefficient on the geometric parameters of the object. The genetic algorithm is used to determine the maximum value of the electromechanical coupling coefficient when changing the radius and thickness of the layers within certain limits. Thus, a set of geometric dimensions is determined to achieve the best PEG energy conversion efficiency. After applying the optimization algorithm, the electromechanical coupling coefficient increases by 34% in comparison with the results of previous studies.

Theoretical Modeling of Piezoelectric Cantilever MEMS Loudspeakers

Piezoelectric microelectromechanical system (MEMS) loudspeakers have received extensive attention in recent years. In particular, the piezoelectric cantilever MEMS loudspeaker, which uses multilayer piezoelectric cantilever actuators (MPCAs), has attracted attention because of its small size, low cost, ease of manufacture, and desirable piston movement. However, owing to the complex driving principles of MPCAs, no adequately efficient and appropriate method currently exists that can be used to analyze and predict the performance of piezoelectric cantilever MEMS loudspeakers. In this study, the equivalent circuit method (ECM) is adopted to theoretically model piezoelectric cantilever MEMS loudspeakers, and an ECM model with a special MPCA transformer for electromechanical conversion is proposed. With the proposed ECM model, the performance characteristics of piezoelectric cantilever MEMS loudspeakers, such as the displacement and sound pressure response, can be calculated efficiently and conveniently. To verify the accuracy of the ECM model, the finite element method is adopted for simulation, and the simulated results are compared with those of the ECM models. A satisfactory agreement was found, which verifies the accuracy of the proposed ECM model.

Fault diagnosis of a squirrel cage induction motor fed by an inverter using lissajous curve of an auxiliary winding voltage

The development of electronic power components has allowed the increase of the induction machine performance. Indeed, monitoring squirrel cage induction motors driven through rectifiers and inverters has been a major concern. This paper presents a new method to diagnose the combination inverter-machine faults. This technic considers an auxiliary winding which is a small coil inserted between two stator phases. It is based on Lissajous curve of this auxiliary winding voltage Park components. For this purpose, modeling the electromechanical conversion chain is necessary. The focus will particularly be on modeling both the squirrel cage induction machine and the inverter in a non-defected case which is a reference. Moreover, the explicit expressions developed for the inserted winding voltage and its Park components will be presented. The simulation results show the effectiveness of the proposed method.

High-performance silicon nanocomposite based ionic actuators

Ultrahigh ion-sensitive silicon nanocomposites are designed for powerful ionic actuators with highly efficient electromechanical conversion and long cycling life.

Enhanced performance of piezoelectric composite nanogenerator based on gradient porous PZT ceramic structure for energy harvesting

Oriented layer and interconnected transverse bridges between layers in the progressive lamellar region effectively improved the electromechanical conversion efficiency.