<![CDATA[The objective of this paper is comparative reviews of PMSM electrical and thermal models for next-generation electric vehicles. The growing demand for electric vehicles (EVs) has necessitated advancements in motor technologies, with Permanent magnet synchronous motors (PMSMs) emerging as a dominant choice for the next-generation EV powertrains due to their high efficiency, compact design, and excellent torque characteristics. However, the performance and reliability of PMSMs in EVs are significantly affected by electrical and thermal behaviors, which are critical for optimizing their efficiency, longevity, and thermal management. This review provides a comprehensive comparison of various electrical and thermal models used to simulate and analyze PMSMs for EV applications. Electrical models focus on accurate representation of motor dynamics, including the influence of control techniques such as Field-Oriented Control (FOC) and Direct Torque Control (DTC). Conversely, the goal of thermal models is to forecast the motor's thermal performance by accounting for heat production, cooling techniques, and how temperature affects electrical and magnetic characteristics. Thermal modeling techniques remain relatively underdeveloped. Most models use simplified lumped parameter thermal networks (LPTNs) or basic steady-state approaches, which fail to capture spatial and temporal temperature gradients across components like windings, stator core, rotor, and bearings. The strengths and limitations of lumped-parameter models, finite element analysis (FEA), and coupled Multiphysics simulations in representing the intricate relationships between the electrical and thermal domains are compared in depth. The study also discusses new advancements, such as the application of machine learning methods for real-time monitoring and model optimization. Lastly, potential prospects for enhancing model fidelity and computing efficiency are outlined, as well as the difficulties in accurately predicting thermal behavior under dynamic operating settings.]]>
