Torque Estimation in Switched Reluctance Machines: A Comprehensive Approach Involving Inductance Modeling Techniques

Abstract

This work highlights advances in the torque estimation method for Switched Reluctance Machines (SRMs), focusing on a high-precision torque estimator that integrates classical and modern modeling techniques. The employed method uses cubic splines and Lagrange polynomials to model the inductance surfaces in order to optimize the estimated instantaneous torque. This approach optimizes drive systems, making SRMs more efficient for critical industrial applications, such as electric vehicle propulsion and renewable energy systems. The method, validated through simulations and experiments, presents an accuracy of about 97% in the reconstruction of the inductance surface, which guarantees the high performance of the estimated torque. The presented results indicate that the high detail of the inductance variations in SRMs contributes positively to the real-time and low-computation torque estimation. Thus, this work contributes to the development of more efficient electric drive control systems, which allow advances in sustainable, accurate and effective industrial applications.