Feature Selection for Enhanced Rotor Temperature Estimation in Permanent Magnet Synchronous Motor for EV Applications

Accurately estimating rotor temperature is crucial for optimizing the performance, efficiency, and reliability of permanent magnet synchronous motors (PMSMs) used in electric vehicle application. Traditional methods for rotor temperature estimation often rely on physical models and sensor-based approaches, which can be complex, costly, and computationally expensive. Despite advancements in data-driven methods, precise rotor temperature estimation remains a challenge due to the need for large datasets and the inclusion of redundant features that can degrade model performance. This paper primarily addresses this challenge by analyzing state-of-the-art feature selection methods in combination with regression models for rotor temperature estimation. Furthermore, a novel reinforcement learning-based wrapper feature selection method is proposed to enhance the regression performance. The proposed method stands out by dynamically selecting the most relevant features from real datasets collected using an experimental PMSM setup and leads to improved estimation accuracy and computational efficiency compared with the state-of-the-art techniques. Moreover, a comprehensive analysis of selected features reveals that temperature-based features are more significant for rotor temperature estimation compared to current and voltage counterparts.