As permanent magnet synchronous motors (PMSM) have high power density, efficiency, good dynamic performance, and small size they are becoming popular in electric vehicle (EV) applications. Control performance and the efficiency of the system get affected due to electrical, mechanical parameters. Parameters value gets affected by voltage source inverter (VSI) non-linearities, temperature and magnetic saturation effects. If exact parameters for particular torque speed requirement are found, the efficiency of system increases. There are various offline and online methods for finding parameters. Offline methods are easy to implement but requires extra setup and estimate parameters in steady state. Because the effects of transient conditions are taken into account during identification, online methods for obtaining real-time data under running conditions are becoming more popular. An overview about online numerical methods to estimate electrical parameters of PMSM is given. It discusses difference between various methods in terms of computational cost, convergence speed, noise and identification error. Choosing of method will be easy using this work. For inductance estimation, the extended Kalman filter (EKF) algorithm has an identification error of 0.24% under temperature effect and -0.3% under VSI non-linearities effect. The identification error for Rs and ψf using the recursive least square (RLS) method is 0.5% and 0.02%, respectively, when temperature is considered. EKF and RLS algorithms are proposed.

Estimation of parameters of Permanent Magnet Synchronous Motor (PMSM) plays an important role for motor controller tuning in Electric Vehicle (EV) application. Under running condition motor parameters vary due to different effects such as temperature, saturation and Voltage Source Inverter (VSI) non-linearities. Identification of parameters in running condition increases the control performance of system. This paper uses Extended Kalman Filter (EKF), which allows estimation of d-q axis inductances of PMSM. The control algorithm considered here is Field Oriented Control (FOC) for EV system having position sensor. The simulation is performed using MATLAB- Simulink software. The simulation results show that EKF identifies the d- and q- axis inductances Ld, Lq considering measurement and process noise using a state space model of motor equations for implementation.