GA-based neural network for energy recovery system of the electric motorcycle

Authors:
Chin-Hsing Cheng;Jian-Xun Ye
Affiliations:
Department of Electrical Engineering, Feng Chia University, No. 100, Wen Hwa Rd., Taichung 407, Taiwan;Department of Electrical Engineering, Feng Chia University, No. 100, Wen Hwa Rd., Taichung 407, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

This paper discusses a regenerative braking system for the electric motorcycle that performs regenerative energy recovery based on neural network control with a boost converter. A constant regenerative current control scheme is proposed, thereby providing improved performance and high energy recovery efficiency at minimum cost. The neural network controller is used to simulate the regenerative system in Matlab/Simulink and neural network toolbox. We can sieve out the suitable training samples to obtain good performance of the controllers, and the neural network with genetic algorithms is used to design the controller. Simulation results of neural network controller show a more steady quality and extended time of charging. The proposed scheme not only increases the traveling distance of the vehicle but also improves the performance and life-cycle of batteries, and the energy recovery of batteries becomes more stable. Therefore, the market of the electric vehicle will become more competitively.