IJCAS

In order to deal with three major problems of electric vehicle (EV): the short driving range, the short life of batteries, and the poor ability of start-up, a hybrid power system was designed and applied to the EV. It was composed of an ultracapacitor with high-specific power and long life, four lead-acid batteries, and a bi-directional DC/DC converter. To improve the stability and reliability of the hybrid-power EV, based on establishing the mathematical models of driving and regenerative-braking processes, a novel neural network sliding mode controller (NNSMC) was researched and designed for the EV. The controller comprises a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), and a sliding mode controller (SMC). The BPNN is used to adaptively ad-just the switching gain of the SMC on-line so as to avoid the whippings. The RBFNN is used to per-form system identification and parameter prediction. The experimental results show that the NNSMC is superior to PID controller at response speed, steady-state tracking error and resisting perturbation whenever driving or braking. Additionally, the hybrid-power EV with NNSMC can improve the ability of start-up, recover more energy, lengthen the life of batteries, and increase the driving range than the EV using batteries as its single power source by about 40%, and than the hybrid-power EV with PID controller by about 4%.

Keywords: Electric vehicle, hybrid power, neural network, regenerative braking, sliding mode control, ultracapacitor.