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期刊论文
Artifical Neural Network Maximum Power Point Tracker for Solar Electric Vehicle
TSINGHUA SCIENCE AND TECHNOLOGY 2005, 10(2)204-208,-0001,():
This paper proposes an artificial neural network maximum power poing tracker (MPPT) for solar electric vehicles. The MPPT is based on a highly efficient boost converter with insulated gate bipolar transis-tor (IGBT) power switch. The reference volatge for MPPT is obtained by artificial neural networ (ANN) with gradient descent momentum algorithm. The tracking algorihm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given insolation, tempera-ture, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by icluding a proportional-integral-derivative (PID) controller, which is also used to match the reference and battery volatage levels. The controller, based on the information sup-plied by the a ANN, genetrates the solar vehicle. The experimental and simulation results show that the propsed schme is highly effcient.
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