Fault-Tolerant Control Strategy for Hall Sensors in BLDC Motor Drive for Electric Vehicle Applications

Abstract

The adoption of the brushless DC motor in the electric drive vehicle industry continues to grow due to its robustness and ability to meet torque–speed requirements. This work presents the implementation of a fault-tolerant control (FTC) for a BLDC motor designed for electric vehicles. This paper focuses on studying the defect in the Ha sensor and its signal reconstruction, assuming possible cases, but the same principle is applied to the other two sensors (Hb and Hc ). In this case, the fault diagnosis allows for the correction and reconstruction of the signal in order to compel the system to work despite the presence of a fault. Indeed, several robust control systems are used within the work to regulate the speed of the motor properly, such as control via fuzzy logic and control via a neural network. This paper presents three BLDC control configurations for EVs, PID, fuzzy logic (FL), and an artificial neural network (ANN), discusses the pros and cons, and develops corresponding mathematical models to enhance a fault-tolerant control strategy which is analyzed and studied using MATLAB-based simulations (by discussing the two cases, the steady state and the transient state), allowing for a novel design based on the analytical models developed. The results obtained from the simulation of this system improved the speed controlled by the neural network compared to the fuzzy logic controller. At the same time, the sensor failure had no effect on the system’s operation due to the efficiency of the FTC control.

PMSM Inter-Turn Short Circuit Fault Detection Using the Fuzzy-Extended Kalman Filter in Electric Vehicles

“…Nevertheless, these motors are susceptible to inter-turn short circuit faults due to various reasons, such as mechanical stresses, temperature rise, and electrical overload [2]. This type of failure represents about 38% of all motor faults [3,4]. Impulsive short circuits may result in elevated magnetic counter fields, thereby augmenting the probability of demagnetization.…”

“…Currently, the knowledge-data diagnostic techniques that have reached a higher level of development and sophistication are Support Vector Machine (SVM), expert systems, neural networks (NN) and fuzzy logic (FL), and deep learning [4,[16][17][18]. The efficacy and resilience of these methods have been demonstrated in detecting faults [19][20][21].…”