Adaptive Signal Filtering and Health Monitoring for Electric Motor Control Systems in New Energy Vehicles

With the widespread adoption of new energy vehicles and the push for environmental policies, the performance and reliability of electric motors, a core component of these vehicles, have become increasingly important.Effective health monitoring of electric motor control systems not only enhances operational efficiency but also extends the motor's lifespan and reduces maintenance costs.Thus, accurately monitoring motor performance and diagnosing faults in a timely manner has become a focal point of current research.However, existing methods show instability under complex operating conditions and suffer from inaccuracies in signal filtering and fault detection.To address these issues, this paper proposes an adaptive signal filtering method based on Extended Kalman Filtering (EKF) combined with magnetic flux estimation and hierarchical transfer learning strategies for health monitoring, aiming to improve model generalization and detection performance.This research provides new technological support for the health monitoring of electric motor control systems in new energy vehicles, offering significant theoretical and practical value.