Torque estimation of electric vehicle motor using adaptive-network based fuzzy inference systems

Authors : Alper KEREM

Pages : 33-41

Doi:10.18245/ijaet.879754

View : 15 | Download : 7

Publication Date : 2021-03-31

Article Type : Research Paper

Abstract :This paper presents to estimating studies of the torque data of the Electric Vehicle insert ignore into journalissuearticles values(EV); motor using Adaptive-Network Based Fuzzy Inference Systems insert ignore into journalissuearticles values(ANFIS);. The real-time data set of the Outer-Rotor Permanent Magnet Brushless DC insert ignore into journalissuearticles values(ORPMBLDC); motor which was designed and manufactured for using in ultra-light EV, was used in these estimation process. The current, the power and the motor speed parameters are defined as input variables, and the torque parameter defined as output variable. Five distinct ANFIS models were designed for torque estimation process and the performances of each model were compared. The most effective model for testing data set among the ANFIS models was anfis: 2 with 98 nodes and 36 fuzzy rules, and the worst model was anfis: 5 with 286 nodes and 125 fuzzy rules. Performance results of all designed models were presented in tables and graphs.
Keywords : electric vehicle motor, adaptive network based fuzzy inference systems ANFIS, outer rotor permanent magnet brushless DC ORPMBLDC, motor, torque estimation