Estimation of Apparent Inductances in Electrically Excited Synchronous Machines Using Enhanced Particle Swarm Optimization

Journal article, 2026

Electrically excited synchronous machines (EESMs) are becoming increasingly preferred in electric vehicles (EVs) due to their non-reliance on rare-earth materials. In traction EESMs, accurate estimation of apparent inductances is crucial for designing current controllers, calculating electromagnetic torques, and determining operation maps. Existing inductance estimation methods, developed based on the permanent magnet synchronous machines (PMSMs), only consider self inductances of the stator windings. However, due to the presence of both stator and field windings in EESMs, it is necessary to estimate both self and mutual inductances, which can greatly increase the estimation difficulty due to more unknown parameters. Hence, in this paper, a new EESM apparent inductance estimation method is proposed. In the proposed method, self and mutual inductances are simultaneously estimated using an enhanced particle swarm optimization (PSO) algorithm. The effectiveness of the proposed method is experimentally verified. Experimental results show that, compared to the standard PSO and dynamic PSO, the enhanced PSO exhibits higher estimation accuracy and better estimation stability. Additionally, by employing the estimated apparent inductances in the EESM current controller, precise current control can be achieved during dynamic processes