3-D Maximum Power Point Searching and Tracking for Ultra Low Power RF Energy Harvesters

2019 ◽  
Author(s):  
Michele Caselli ◽  
Andrea Boni
Keyword(s):  
Low Power ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Ultra Low Power ◽  
Energy Harvesters ◽  
Rf Energy ◽  
Power Point

scholarly journals Experimental Measurements of a Low Power CMOS Analog MPPT Power Conditioning Circuit for Energy Harvesting Applications

2021 ◽  
Vol 14 ◽  
pp. 1192-1197
Author(s):  
Francarl Galea ◽  
Owen Casha ◽  
Ivan Grech ◽  
Edward Gatt ◽  
Joseph Micallef
Keyword(s):  
Low Power ◽  
Maximum Power ◽  
Threshold Region ◽  
Power Conditioning ◽  
Maximum Power Point ◽  
Power Circuit ◽  
Ultra Low Power ◽  
Energy Harvesters ◽  
Power Point ◽  
Conditioning Circuit

This paper presents the complete measured performance and characterization of a fabricated power conditioning integrated circuit for energy harvesters with on-chip maximum power point tracking (MPPT) and external energy storage. This ultra-low power circuit employs an AC/DC-to-DC converter compatible with both AC and DC voltage energy harvesters. The MPPT design follows the perturbation and observation algorithm. This MPPT is capable of tracking the maximum power point of types of energy harvesters. The circuit is implemented using the AMS CMOS 0:35 μm high voltage technology and all the circuit blocks use analog electronic techniques, with the transistors operating in the sub-threshold region, in order to obtain a minimum power consumption. This power conditioning circuit consumes less than 2 μW while featuring an input voltage range of -0:5V to -50V and a power range from 10 μW to 200mW.

Design of a Smart Maximum Power Point Tracker (MPPT) for RF Energy Harvester

2020 ◽  
Vol 29 (01n04) ◽  
pp. 2040006
Author(s):  
Dilruba Parvin ◽  
Omiya Hassan ◽  
Taeho Oh ◽  
Syed Kamrul Islam
Keyword(s):  
Energy Harvester ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Energy Harvesters ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Mppt Controller ◽  
Rf Energy ◽  
Power Point ◽  
Rf Energy Harvester

Continuous enhancement of the performance of energy harvesters in recent years has broadened their arenas of applications. On the other hand, ample availability of IoT devices has made radio frequency (RF) a viable source of energy harvesting. Integration of a maximum power point tracking (MPPT) controller in RF energy harvester is a necessity that ensures maximum available power transfer with variable input power conditions. In this paper, FPGA implementation of a machine learning (ML) model for maximum power point tracking in RF energy harvesters is presented. A supervised learning-based ML model-feedforward neural network (FNN) has been designed which is capable of tracking maximum power point with optimal accuracy. The model was designed using stochastic gradient descent (SGD) optimizer and mean square error (MSE) loss function. Simulation results of the VHDL translated model demonstrated a good agreement between the expected and the obtained values. The proposed ML based MPPT controller was implemented in Artix-7 Field Programmable Gate Array (FPGA).

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