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

Year 2021, , 33 - 41, 31.03.2021

Alper Kerem

https://doi.org/10.18245/ijaet.879754

Cited By: 2

Abstract

This paper presents to estimating studies of the torque data of the Electric Vehicle (EV) motor using Adaptive-Network Based Fuzzy Inference Systems (ANFIS). The real-time data set of the Outer-Rotor Permanent Magnet Brushless DC (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

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