A Neural Network Assistance AMPPT Solar Energy Harvesting System With 89.39% Efficiency and 0.01–0.5% Tracking Errors

This paper presents a high-performance solar energy harvesting system with improved adaptive maximum power point tracking (AMPPT) method utilizing neural network (NN) model as assistance. Under the guidance of the negative feedback control (NFC) model, a BJT based voltage control oscillator with three off-chip reconfigurable resistors is designed in this paper to improve the reusability of the solar energy harvesting system. Meanwhile, the AMPPT accuracy has much improvement by co-simulation of MATLAB/Simulink and Virtuoso with the help of NN model of Photovoltaic (PV) Cell. The complete system with output voltage of 4.2V to power the battery is designed and fabricated in
$0.18~\mu \text{m}$
CMOS technology. According to the test results, the system can track the maximum power points (MPP) successfully with average voltage tracking errors of 0.23% (0.01-0.51%) of PV cell 1 and 0.29% (0.01-0.5%) of PV cell 2 when the light intensity changes from 3000lux to 10000lux. Without power hungry current sensor or voltage sensor and other complicated control circuits, the peak efficiency is about 89.39% @ 3000lux.