Due to the increasing demand of electrical vehicles (EVs), prognostics of the battery state is of paramount importance. The nonlinearity of the signal (e.g. voltage) results in the complexity of analyzing the degradation of the battery. Aging characteristics extracted from the voltage, current, and temperature when the battery is fully charged/discharged were commonly used by previous researchers to determine the battery state. The drawbacks of the previous prediction algorithms are insufficient or irrelevant features to explicitly model the battery aging and the use of fully charged/discharged datasets, which might result in poor prediction accuracy. Therefore, this study proposes a feature selection technique to adequately select optimum statistical feature subset and the use of partial charge/discharge data to determine the battery remaining useful life (RUL) using Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM). The proposed approach demonstrated exceptional RUL prediction results, with the root mean square error (RMSE) of 0.00286 and mean average error (MAE) of 0.00222 using partial discharge data. The proposed method shows prediction improvement in comparison with the use of full data and state-of-the-art outcomes from previous studies of the same open data from the National Aeronautics and Space Administration (NASA) prognostic battery data sets. INDEX TERMS Recurrent neural network, long short-term memory, remaining useful life, battery management systems, feature selection.
Most screw-thread-type ultrasonic motors are designed to be two-phase driven. This paper aims to present a novel single phase driven design that generates the required wobble motion, thus significantly simplifying the driving circuit of the ultrasonic motor. The proposed single-phase driven screw-thread-type ultrasonic motor works with two orthogonal bending modes generated by an asymmetric stator design. The novel stator design can improve the vibration displacement and further enhance the performance of the single phase driven motor. The vibration characteristics of the asymmetric stator structure were analyzed by ANSYS finite element analysis software. Based on the design and analysis processes, a prototype of the desired screw-thread-type ultrasonic motor was fabricated and tested. When the operating voltage is 200 Vpp, the obtained main characteristics of the proposed motor are as follows: the working frequency is between 28.3 and 29.5 kHz; the maximum no-load velocity is approximately 4.1 mm s(-1); and the thrust force is 1.6 N.
In recent years, many motor fault diagnosis methods have been proposed by analyzing vibration, sound, electrical signals, etc. To detect motor fault without additional sensors, in this study, we developed a fault diagnosis methodology using the signals from a motor servo driver. Based on the servo driver signals, the demagnetization fault diagnosis of permanent magnet synchronous motors (PMSMs) was implemented using an autoencoder and K-means algorithm. In this study, the PMSM demagnetization fault diagnosis was performed in three states: normal, mild demagnetization fault, and severe demagnetization fault. The experimental results indicate that the proposed method can achieve 96% accuracy to reveal the demagnetization of PMSMs.
When a driving voltage opposite to the piezoelectric polarity is applied on the flextensional stator, it will generate the normal force, of which the operating voltage range of piezoelectric actuators will decrease. This paper presents a novel stator design for producing the normal force in which the driving voltage has the same piezoelectric polarity, which is based on the structure of two multilayer piezoelectric actuators clamped in a star-shaped shell. To obtain the two close resonance frequencies of flexural and translation modes, a genetic algorithm combined with the finite element analysis is employed to find the optimal dimensions for the geometry of the stator. The importance of each design parameter is evaluated through a proposed sensitivity analysis method. A prototype resulting from the optimal design was fabricated and the experimental results are given to show that the stator can generate, in practice, the required coupling resonance mode between 35.15 kHz and 36.49 kHz.
Recent advances in measurement systems require positioning systems with high stiffness, accuracy and speed. Piezoelectric actuators which are featured with mechanical simplicity, quick response, and electromagnetic immunity, are often used in precision positioning. It is known that piezoelectric actuators can achieve high positioning accuracy by the stepping mode but low speed. By contrast, the resonance vibration mode will offer high positioning speed, but sacrifices the high inherent position resolution. For the stepping mode, the displacement of the piezoelectric actuator significantly affects the speed, of which larger displacement induces higher speed. For the resonance vibration mode, an elliptical motion of the piezoelectric actuator tip is generated by horizontal and vertical eigenmodes, and the optimal efficiency can be achieved when the two eigenmodes are operated at the same frequency. For the applications of high positioning accuracy and speed, a piezoelectric actuator should be designed by taking these two operation modes into consideration simultaneously. Based on these requirements, the optimal structural dimensions of a piezoelectric actuator are obtained using a genetic algorithm.
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