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Amesim Boost Chopper

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Electrodynamic Vibration Energy Harvesting for IoT/IIoT - with State-Average Boost Converter

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Description

This example is intended to show relevant modeling and simulation capabilities of SystemVision Cloud, for electrodynamic energy harvesting (EH) systems. It is not necessarily a practical EH design itself, but rather demonstrates the tool's ability to support knowledgeable users who are creating practical designs.The mechanical and magnetic circuit sections of the model are composed of "physical" models, in that user can directly specify size and physical properties of the components. This includes the mass of the armature, the stiffness of the resonant spring, the cross section area and length of the magnetic core, the residual flux density of the permanent magnet, and the number of winding turns.The electronics section (rectifier and boost converter) contains a mix of passive analog circuit elements as well as abstract or "math block" models to represent the state-average (non-switching) behavior of the converter. Finally, constant power load model represents the power demand for periodic transmission of data typical of an (I)IoT sensor node.In the simulation results displayed on the schematic, the upper right waveform viewer shows the amplitude of the external vibration source (e.g. a motor or transformer housing) of 0.07mm peak at 60 Hz, equivalent to a peak acceleration of 1g (light-blue waveform). The armature spring-mass resonance frequency is 60 Hz, so the armature displacement is seen to reach the frame's travel limit of 4mm peak-to-peak (green waveform).In the upper left, the two waveform viewers are zoomed-in near the 1 second simulation time mark, and they show the air-gap lengths and the corresponding core flux density and winding voltage. Note that the air-gaps are configured in parallel for the flux path, so the effective path reluctance is minimized when either gap approaches zero length.In the lower right, the relatively low value of the rectified "DC" voltage is observed (red waveform), as well as the regulated boost output voltage that supplies the transmitter load.

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Boost converter

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Boost (DC-DC Converters)

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Virtual Evaluation Board for NCV8876

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DC_DC_BLOCK_BOOST_L_3

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DC_DC_BLOCK_BOOST_GIVEN_MOS

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12V Solar Panel with Boost Converter and MPPT Algorithm for Charging 48V Battery

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Description

This battery charging example uses a Maximum Power Point Tracking (MPPT) algorithm combined with an ideal boost converter. The solar panel model in this example allows the user to specify not only the "boilerplate" electrical characteristics (i.e. the open circuit voltage, short circuit current and peak power output capability), which are always given at the full or nominal irradiance level, but also to specify the reduced output and shift in the peak power point at lower irradiance levels. This shift in the load voltage at which peak power transfer occurs gives rise to the performance improvement that can be achieved with MPPT (maximum power point tracking) when the irradiance level varies over time.The "electronics" section of this design consists mainly of abstract "math block" models. These are used to represent the state-average (non-switching) behavior of the converter. The sampled-data MPPT algorithm dynamically adjusts the boost converter's duty-cycle, to keep the solar panel operating at its peak power output. The user can adjust various "tuning" parameters of that algorithm (e.g. the sample rate, the duty-cycle "delta" or perturbation used for tracking, etc.). The simple algorithm is visible in the open-source "MPPT-Solar" model, just right click and select "View/Copy Model". The simulation results show that the panel output current level needs to adjust over a very wide range, from 9.3A down to 1.0A, for the panel to remain at the peak power operating point as the relative irradiance level varies from 100% down to 20%, respectively.

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buck-boost

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Dual Boost Topology

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