D.T. Lee, S.J. Shiah, C.M. Lee (Taiwan), and C.H. Wu (USA)
Electric Scooter, Intelligent Control, Muscle-like Compliant Control, Fuzzy Neural Network, Cerebellar Model Articulation Controller
Most of the present day electric scooters are equipped with a voltage-driven DC motor powered by four 12-volt lead-acid batteries and a hand-lever accelerator operated by the rider to control their speed. Because of the nonlinear battery discharge characteristics and different driving behaviors of riders, it is not easy to tell how much electric power remaining in the battery and how far the electric scooter can travel before the battery has to be re-charged. As a result, the reliability of the electric scooter is lacking. To tackle this problem and to enhance the capabilities of present electric scooters, we propose an intelligent control system that not only can control the speed of the electric scooter, but also can provide information about residual electric power in the battery system by monitoring its power consumption. This system consists of both motor driver control and energy management subsystems. The driver control subsystem is implemented as a closed-loop speed control system by using a muscle-like control law with excellent compliant property. The energy management subsys tem is implemented by learning modules based on fuzzy neural networks and cerebellar model articulation controller networks, which can estimate and predict nonlinear characteristics of the power consumption of batteries and electric scooter dynamics. With this battery power monitoring subsystem the rider will be provided information regarding an estimated traveling distance at a given speed, and the maximum allowable speed to guarantee safety arrival at the destination with the residual battery capacity. Experimental results show that the performance of electric scooters can be improved substantially.
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