Simulation Research on Neural Network Sliding Mode Control of Energy-Regenerative Braking of Electric Vehicle

Abstract:

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To improve the energy-regenerative efficiency and robustness of electric vehicle (EV), a novel energy-regenerative controller was designed and applied to the charge current loop of the EV. The controller that combines neural network (NN) with traditional sliding mode controller (SMC) comprises a radial basis function NN (RBFNN) and a SMC. The RBFNN is used to adaptively adjust the switching gain of the SMC. The simulation model of the energy-regenerative system is built with MATLAB/SIMULINK, and the simulation results show that comparing with traditional SMC, the NNSMC has better performance at response speed, steady-state tracking error and resisting disturbance in energy-regenerative process. Additionally, it can recover more energy.

Info:

Periodical:

Edited by:

Yi-Min Deng, Aibing Yu, Weihua Li and Di Zheng

Pages:

1187-1190

DOI:

10.4028/www.scientific.net/AMM.37-38.1187

Citation:

J. B. Cao et al., "Simulation Research on Neural Network Sliding Mode Control of Energy-Regenerative Braking of Electric Vehicle", Applied Mechanics and Materials, Vols. 37-38, pp. 1187-1190, 2010

Online since:

November 2010

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$35.00

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