Paper Title:
Simulation Research on Neural Network Sliding Mode Control of Energy-Regenerative Braking of Electric Vehicle
  Abstract

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, S. J. E, X. L. Zhu, H. K. Jiang, B. G. Cao, "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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