Akademik Veri Yönetim Sistemi
2022 8th International Conference on Control, Decision and Information Technologies (CoDIT), İstanbul, Türkiye, 17 Mayıs 2022, cilt.1, ss.1091-1095
In this study, the development of electric motor design optimization
methods and algorithms for electric vehicles, which have become
widespread as a result of energy policies, is discussed. The rapidly
increasing need for micro transportation within the scope of small
cities has increased the interest in short-range transportation vehicles
such as electric bicycles and electric scooters. Therefore, an electric
scooter model is considered and the desired motor requirements are
determined by analyzing its dynamic model. Then, IPM topologies are
investigated and the appropriate topology is decided. IPM design
parameters are dealt with in the ANSYS RMXprt environment, and all
design combinations by selecting the appropriate test matrix in
Taguchi's experiment design method are modeled in ANSYS RMXprt and
logged in the appropriate file format together with the obtained
results. The motor design models of all experiments are saved as the.
png format in the aspect format to be determined. Then, the labeled
pictures with the obtained results in the experimental design are
trained in MATLAB on a neural network model with appropriate input and
output. Thereafter, the trained neural network derives the appropriate
motor geometry in terms of the design requirements. The derived motor
geometry is converted into a 2D technical drawing format with the help
of a package program (Img2CAD) and uploaded to the ANSYS Maxwell
environment. To assess the motor performance are performed in ANSYS
Maxwell. The proposed methodology shows that the results of parameter
estimation and geometry generation in solution space with the trained
neural network give sufficient performance.