Condition Monitoring and Fault Diagnosis of BLDC Motor in Electric Vehicles Using Artificial Intelligence

Due to their high benefits in terms of environment and standards of battery advancement, Electric Vehicles (EVs) are extremely important in the shift towards transportation. Here the Brushless DC (BLDC) motor is an integrated part of the EVs, and it has become the most popular choice due to better efficiency, reliability, and reduced maintenance requirements than the others. The major difference from traditional motors is that BLDC motors make use of electronic commutation, so brushes are not employed; this results in lower amounts of wear. This not only helps the motor to have a longer working life but also to work more efficiently, or to maintain their efficiency. However, one crucial drawback to using BLDC motors is the fact that they tend to perform and have great durability if the condition monitoring and fault diagnosis systems when utilized top-notch. These systems are important for the identification of abnormalities and possible defects that if not attended to can lead to major failures and long periods of non-productive time. The ability to track the state of BLDC motors in real time enables one to have preventive measures that cut the chances of the motor breaking down and consequently the costs attached to this eventuality. Condition monitoring involves continuous assessment of motor attributes such as; vibration, temperature, and electrical patterns in a bid to detect changes in healthy motor status. Whereas the existing literature on the level of faults goes beyond it by identifying the specific nature and location of the faults. Altogether, the procedures composed play the basic role of a predictive maintenance program and check BLDC motors’ performance and reliability throughout their usage. Given the importance of BLDC motors in EVs, improvements in the reliability of condition monitoring and fault diagnosis are unpreventable for the enhancement and sustainability of electric mobility.