Hardware-in-the-Loop Scheme of Linear Controllers Tuned through Genetic Algorithms for BLDC Motor Used in Electric Scooter under Variable Operation Conditions

• Published in: Machines (Basel) 2023-06, Vol.11 (6), p.663
• Main Authors: Moreno-Suarez, Leonardo Esteban, Morales-Velazquez, Luis, Jaen-Cuellar, Arturo Yosimar, Osornio-Rios, Roque Alfredo
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; BLDC; Brushless motors; Climate change; Control systems design; Controllers; Cost control; D C motors; Design improvements; Design optimization; Electric bicycles; Electric motors; Electric vehicles; Electrical loads; electrical scooter; Energy consumption; Field programmable gate arrays; Genetic algorithms; Hardware; hardware in-the loop; Heuristic methods; Magnets, Permanent; micro-mobility; Motor scooters; Neural networks; Permanent magnets; Power electronics; Public transportation; Simulation; Synchronous motors; Velocity; Wheelchairs; Wind speed
• ISSN: 2075-1702; 2075-1702
• DOI: 10.3390/machines11060663

Outrunner brushless DC motors (BLDC) are a type of permanent magnet synchronous motor (PMSM) widely used in electric micro-mobility vehicles, such as scooters, electric bicycles, wheelchairs, and segways, among others. Those vehicles have many operational constraints because they are driven directly by the user with light protective wearing. Therefore, to improve control strategies to make the drive safer, it is essential to model the traction system over a wide range of operating conditions in a street environment. In this work, we developed an electro-mechanical model based on the Hardware-in-the-Loop (HIL) structure for a two-wheeler electric scooter, using the BLDC motor to explore its response and to test linear controllers for speed and torque management under variable operating conditions. The proposed model includes motor parameters, power electronics component characteristics, mechanical structure, and external operating conditions. Meanwhile the linear controllers will be adjusted or tuned though a heuristic approach based on Genetic Algorithms (GAs) to optimize the system’s response. The HIL scheme will be able to simulate a wide range of conditions such as user weight, slopes, wind speed changes, and combined conditions. The designed model can be used to improve the design of the controller and estimate mechanical and electrical loads. Finally, the results of the controller tests show how the proposed cascade scheme, tuned through the GA, improves the system behavior and reduces the mean square error with respect to a classical tuning approach between 20% and 60%.