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.

中文翻译：

---

一种具有 89.39% 效率和 0.01-0.5% 跟踪误差的神经网络辅助 AMPPT 太阳能收集系统

本文提出了一种利用神经网络 (NN) 模型作为辅助的改进的自适应最大功率点跟踪 (AMPPT) 方法的高性能太阳能收集系统。在负反馈控制（NFC）模型的指导下，本文设计了一种基于 BJT 的具有三个片外可重构电阻器的压控振荡器，以提高太阳能收集系统的可重用性。同时，借助光伏（PV）电池的神经网络模型，通过MATLAB/Simulink和Virtuoso的联合仿真，AMPPT精度有了很大的提高。为电池供电的输出电压为 4.2V 的完整系统采用 $0.18~\mu\text{m}$ CMOS 技术设计和制造。根据测试结果，系统能够成功跟踪最大功率点（MPP），平均电压跟踪误差为0。当光强从3000lux变为10000lux时，PV电池1的23%（0.01-0.51%）和PV电池2的0.29%（0.01-0.5%）。无需耗电电流传感器或电压传感器等复杂控制电路，峰值效率约为89.39%@3000lux。