DIAGNÓSTICO DE FALLA ELÉCTRICA DE ESTATOR EN MOTORES BLDC DE VEHÍCULO LIGERO EN DIFERENTES REGÍMENES DE VELOCIDAD MEDIANTE LA TRANSFORMA DISCRETA DE FOURIER (STATOR’S ELECTRIC FAULT DIAGNOSIS IN BLDC MOTORS OF LIGHT VEHICLE IN DIFFERENT VELOCITY REGIMES BY MEANS OF THE DISCRET FOURIER´S TRANSFORM)

Resumen El presente trabajo muestra un esquema de diagnóstico de fallas eléctricas del estator en diferentes regímenes de velocidad y par constante en motores tipo BLDC de vehículos eléctricos ligeros mediante el análisis de tres escenarios diferentes de velocidad con una falla en el devanado del estator de la fase C. Para efectuar el diagnóstico de la falla se adquiere información eléctrica de las corrientes del estator y mediante el uso del fasor de espacio instantáneo de Park (ISP, por sus siglas en inglés) se analiza el módulo de dicho fasor mediante la técnica tiempo-frecuencia conocida como transformada rápida de Fourier (FFT, por sus siglas en inglés), lo cual permite identificar la firma de fallas eléctrica del estator en función de su velocidad para cada uno de los escenarios de velocidad estudiados. Palabras Clave: Diagnóstico de falla, fasor de espacio instantáneo, motor BLDC, transformada rápida de Fourier. Abstract In this work a scheme of stator’s electric fault diagnosis is showed in different velocity regimes and constant torque in motors type BLDC of electric light vehicles by means of three different stages velocity analysis with a stator’s winding fault on phase C. To be made fault diagnosis the stator´s electrical currents information is acquired and by to use the instantaneous space phasor (ISP), the modulus of its phasor is analyzed through the time-frequency technique known as fast Fourier transform, thus allows identity the stator’s signature fault in function of its velocity for each of one velocity-schemes studied. Keywords: BLDC motor, fast Fourier transform, fault diagnosis, instantaneous space phasor

Stability analysis and speed control of brushless DC motor based on self-ameliorate soft switching control methods

In recent years, electric vehicles are the large-scale spread of the transportation field has led to the emergence of brushless direct current (DC) motors (BLDCM), which are mostly utilized in electrical vehicle systems. The speed control of a BLDCM is a subsystem, consisting of torque, flux hysteresis comparators, and appropriate switching logic of an inverter. Due to the sudden load torque variation and improper switching pulse, the speed of the BLDCM is not maintained properly. In recent research, the BLDC current control method gives a better way to control the speed of the motor. Also, the rotor position information should be the need for feedback control of the power electronic converters to varying the appropriate pulse width modulation (PWM) of the inverter. The proposed optimization work controls the switching device to manage the power supply BLDCM. In this proposed self-ameliorate soft switching (SASS) system is a simple and effective way for BLDC motor current control technology, a proposed control strategy is intended to stabilize the speed of the BLDCM at different load torque conditions. The proposed SASS system method is analyzing hall-based sensor values continuously. The suggested model is simulated using the MATLAB Simulink tool, and the results reveal that the maximum steady-state error value achieved is 4.2, as well as a speedy recovery of the BLDCM's speed

Inverter Fault Diagnosis of an Electrical Series-Connected Two Sinusoidal Six-Phase Permanent Magnet Machines Drive

This paper investigates a real-time fault diagnostic of a transportation system which needs two drives with fault-tolerance capabilities. Because of constraints on the mass of the system and on the cost of the Voltage Source Inverter (VSI), a drive with two Six-Phase Permanent Magnet Synchronous Machines (PMSM) in series-connection supplied by two six-leg inverters is chosen. Despite the serial -connection, independent control of the two machines and fault –tolerance to open-switch fault is ensured. Nevertheless, a Fault Detection Identification (FDI) process is required for analysis and/or control reconfiguration. The proposed FDI is based on the combination of different criteria obtained from the two zero-sequence currents and from the normalized currents mapped into two frames defined by the Concordia Transformation. Results obtained from simulation and experimental tests show the effectiveness of the proposal.Projet CE2

Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance

Distributed drive electric vehicles are regarded as a broadly promising transportation tool owing to their convenience and maneuverability. However, reasonable and efficient allocation of torque demand to four wheels is a challenging task. In this paper, a deep reinforcement learning-based torque distribution strategy is proposed to guarantee the active safety and energy conservation. The torque distribution task is explicitly formulated as a Markov decision process, in which the vehicle dynamic characteristics can be approximated. The actor-critic networks are utilized to approximate the action value and policy functions for a better control effect. To guarantee continuous torque output and further stabilize the learning process, a twin delayed deep deterministic policy gradient algorithm is deployed. The motor efficiency is incorporated into the cumulative reward to reduce the energy consumption. The results of double lane change demonstrate that the proposed strategy results in better handling stability performance. In addition, it can improve the vehicle transient response and eliminate the static deviation in the step steering maneuver test. For typical steering maneuvers, the proposed direct torque distribution strategy significantly improves the average motor efficiency and reduces the energy loss by 5.25%–10.51%. Finally, a hardware-in-loop experiment was implemented to validate the real-time executability of the proposed torque distribution strategy. This study provides a foundation for the practical application of intelligent safety control algorithms in future vehicles