Recent Trends in Electrical Machines and Drives ISSN: 3139-2067 (Online)

ABSTRACT

Thermal management is a critical factor influencing the performance, reliability, and lifetime of electric drive systems. Excessive temperature rise in electrical machines, power electronic converters, and insulation materials leads to accelerated aging, efficiency degradation, and potential system failure. Traditional thermal modeling techniques rely on physics-based lumped parameter or finite-element models, which often require precise parameter knowledge and are computationally intensive. Recent advances in data-driven methods and digital twin technology have enabled real-time thermal estimation, predictive analysis, and intelligent thermal control of electric drive systems. This paper presents a comprehensive study on data-driven thermal management techniques and the role of digital twins in modern drive systems. Machine learning models, sensor fusion, and real-time simulation frameworks are discussed along with their integration into digital twin architectures.

 KEYWORDS: - Thermal management, digital twin, electric drives, data-driven modeling, temperature prediction, machine learning, reliability