Parametric dataset-based formulation of rated torque of a brushless DC motor for electric vehicle

This study aims to determine the mechanical response of a brushless DC motor (BLDC) used in two-wheeled electric vehicles by analyzing torque values through finite element analysis. The motor features a three-phase stator structure and four permanent magnets on its rotor. The study investigates the relationship between torque, pulse degree, excitation voltage, and stator current using RMxprt software. A parametric dataset consisting of 600 data points is generated to model the BLDC motor system. Genetic programming (GP) is employed to establish a formula that correlates the motor’s output torque with the input variables. The resulting simplified formula, created with GP, achieves a mean absolute percentage error (MAPE) of 0.085 and an R -squared ( R 2 ) value of 0.989, indicating high accuracy in torque prediction based on simulation parameters. This research provides a torque formulation based on parametric finite element analysis, offering potential benefits for electric bicycles and potentially eliminating the need for certain sensors. Thus, before experimental studies in the process of determining BLDC motor torque behavior, a dataset approach based on FEA parametric simulation studies and a GP formulation developed based on the dataset obtained from parametric simulations were proposed.