Regenerative-Braking Sliding Mode Control of Electric Vehicle Based on Neural Network Identification

Abstract

Aiming at the main problems of electric vehicle (EV): short driving range, short life of batteries, and variation of model parameters, based on constructing the main circuit diagram of the EVpsilas control system, the mathematical model of regenerative-braking process was established, and a novel regenerative-braking controller was designed, which combined neural network (NN) with traditional sliding mode controller (SMC). The controller comprises a back propagation NN (BPNN), a radial basis function NN (RBFNN) and a SMC. The BPNN is used to adaptively adjust the switching gain of the SMC on-line so as to avoid the whippings. The RBFNN is used to perform system identification and parameter prediction. The experimental results show that the NNSMC could improve the stability and reliability of the system, and is superior to traditional SMC at response speed, steady-state tracking error and resisting disturbance in the regenerative-braking process. Additionally, it can recover more energy, lengthen batteriespsila life, and increase the driving range than SMC by about 6%. This is very significant for saving energy.