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

Jian Bo Cao; Shi Ju E; Xi Lin Zhu; Hong Kui Jiang; Bing Gang Cao

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

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 37-38Simulation Research on Neural Network Sliding Mode...

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.

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Periodical:

Applied Mechanics and Materials (Volumes 37-38)

Pages:

1187-1190

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.37-38.1187

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Online since:

November 2010

Authors:

Jian Bo Cao, Shi Ju E, Xi Lin Zhu, Hong Kui Jiang, Bing Gang Cao

Keywords:

Electric Vehicle (EV), Energy-Regenerative, Neural Network (NN), Simulink, Sliding Mode Control (SMC)

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