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The rapid adoption of electric vehicles (EVs) has driven the industry to develop advanced traction systems for improved performance and dependability. Such criteria are determined mainly by the magnetic materials used in brushless DC or AC motor commonly used in EV traction systems. More complex motor designs, such as those with two rotors or two stators, have been influenced by improvements in traction systems for transportation tools. The electric motor with multiple rotors or stators has a complex system and lies on parameters that change over time with complex dynamics. Consequently, this motor type needs an intelligent controller to enhance its efficiency and robustness under various drive conditions. Therefore, this article aims to resolve some control complexity for one of these recently used motors. The goal of this is to maximize the efficiency while also increasing motor torque. This motor design is based on a double-stator single-rotor (DSSR) form. The global traction chain is described in this paper by showing the specifications of each part as the interior motor architecture and a suitable AC/DC converter. Examining the best control tool for managing this machine is based on the control topology by using on the field-oriented control (FOC) strategy. A comparison of the effects of three distinct speed controllers, including fuzzy logic, neural networks, and traditional PI controllers, was used to support the FOC architecture. The global traction system architecture and its corresponding control scheme were established on the MATLAB/Simulink platform, and all results were depicted to compare the diverse architectures. At the end of this study, the given results and their corresponding discussions show each control solution's benefits and drawbacks and prove the importance of choosing the appropriate control system for the DSSR motor on the battery state of charge and then on the vehicle autonomy. [ABSTRACT FROM AUTHOR]

A hysteresis current controller (HCC) is commonly used in high-performance AC motor drives to control the current directly. Recently, the predictive current controller (PCC) is also used as an alternative to the classical current controller for speed and torque regulation of induction motor (IM) drives. However, PCC has drawbacks of large flux and torque ripples, large total harmonic distortions (THDs) in current and voltage and dependency on parameters. This paper proposes current control with artificial neural network (ANN) for a field-oriented controlled induction motor (FOCIM) drive. The ANN has input current error between the reference and the measured stator currents. The output function of neuron is a hyperbolic tan (or tan-sigmoid) function to apply error Levenberg–Marquardt (L–M) back propagation as learning rule because of its fast convergence. The proposed method is based on a new approach in which hysteresis band is replaced by ANN comparator to improve the performance of the FOCIM drive. It minimizes torque ripples, flux ripples, voltage and current THDs over the existing HCC and PCC methods. The superiority of the proposed method compared to existing methods is established by simulation and experimental results. [ABSTRACT FROM AUTHOR]

This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%. [ABSTRACT FROM AUTHOR]

The solar water pumping system is one of the brightest applications of solar energy for its environmental and economic advantages. It consists of a photovoltaic panel which converts solar energy into electrical energy to operate a DC or AC motor and a battery bank. The photovoltaic power fluctuation can affect the water pumping system performances. Thus, the photovoltaic power prediction is very important to ensure a balance between the produced energy and the pump requirements. The prediction of the generated power depends on solar irradiation and ambient temperature forecasting. The purpose of this study was to evaluate various methodologies for weather data estimation namely: the empirical models, the feed forward neural network and the adaptive neuro-fuzzy inference system. The simulation results show that the ANFIS model can be successfully used to forecast the photovoltaic power. The predicted energy was used for the solar water pumping management algorithm. • Temperature and solar irradiation forecasts are done for PV power estimation. • Ghouard's empirical model is the best for solar radiation forecasts on sunny days. • Capderou's empirical model is the best for solar radiation forecasts on cloudy days. • The ANFIS outperforms the empirical models and the FFNN in weather data prediction. • Based on the predicted PV power, water pumping management algorithm is done. [ABSTRACT FROM AUTHOR]

The article presents the artificial neural network for online tuning of a genetic algorithm-based proportional-integral (IP) controller for an interior permanent magnet synchronous motor (IPMSM) drive. The controller is designed to achieve accurate speed control of the IPMSM drive under system disturbances. To optimize the PI controller parameters in a closed-loop vector control scheme, a genetic algorithm is used.