Open AccessNovel Adaptive Control Method for BLCD Drive of Electric Bike for Vietnam Environment
Author(s) -
Chuong Nguyen Khanh,
Sachintha Balasooriya,
Ilya Kavalchuk,
A F Kolbasov,
К. Е. Карпухин,
А. С. Теренченко
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/819/1/012017
Subject(s) - drivetrain , automotive engineering , particle swarm optimization , controllability , computer science , electric motor , pid controller , electric vehicle , key (lock) , engineering , control engineering , torque , electrical engineering , physics , mathematics , machine learning , thermodynamics , temperature control , power (physics) , computer security , quantum mechanics
Electric bikes are a rising mode of transportation in developing countries as their use reduce the use of hydrocarbon-based energy sources as well as increases convenience for the commuters within the city by reducing traffic. Furthermore, the electric bikes have low noise emission and no exhaust gases produced, which improves the environmental footprint. Due to the Vietnam regulations, most electric bikes use brushless wheel-hub motors as the main drivetrain solution paired with various types of batteries. The key challenges in the design of the drivetrains are related to the efficiency and controllability areas, so the response of the drivetrain can be improved. The key area of improvement lies in the control system design to optimize the performance and energy consumption. This paper presents the research done on developing of optimization PID control algorithms by using GA (Generic Algorithm) and PSO (Particle Swarm Optimization) for driving a synchronized brushless DC motor for the electric bikes.