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Copy of Electrothermal Energy Harvesting - MPPT Capacitor Charging - on Sat, 10/10/2020 - 21:40

karthik20041999
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karthik20041999

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This example is intended to show relevant modeling and simulation capabilities of SystemVision Cloud for Electrothermal 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 example also illustrates using a sampled-data algorithm for maximum power point tracking (MPPT), to optimize the energy harvest for changing operating temperatures.

The design includes a thermoelectric generator (TEG) that is supplied on the "hot" side by a sinusoidally time varying temperature between 75 degC and 100 degC. The "cold" side is held at a fixed 25 degC. The thermal resistance and heat capacitance of the hot-side heat-sink are shown in the schematic. The electronics section includes a mix of analog circuit elements, including an inductor, 1.0 F super-capacitor, LDO regulator and a periodically switched load resistor. It also includes abstract or "math block" models to represent the state-average (non-switching) behavior of a buck-boost converter.

The goal of the design is to extract sufficient power from the TEG, to provide a 2.5-Watt/1-second duration power burst once every 10 seconds. This burst is presumably to supply power for a periodic data transmission. The simple MPPT algorithm that helps achieves this is visible in the open-source MPPT-TEG model shown. The MPPT algorithm dynamically adjusts the load current draw from the TEG, to keep it operating at its maximum power output capability. That capability varies with the differential operating temperature. That shift can more easily be seen in the followingTEC/TEG calibration test schematic:

https://www.systemvision.com/design/calibrate-tecteg-energy-harvesting

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energy

lucas.camacho.2018.br
Designer235362

lucas.camacho.2018.br

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4 years 2 months

9

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Description

This example is intended to show relevant modeling and simulation capabilities of SystemVision Cloud for Electrothermal 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 example also illustrates using a sampled-data algorithm for maximum power point tracking (MPPT), to optimize the energy harvest for changing operating temperatures.

The design includes a thermoelectric generator (TEG) that is supplied on the "hot" side by a sinusoidally time varying temperature between 75 degC and 100 degC. The "cold" side is held at a fixed 25 degC. The thermal resistance and heat capacitance of the hot-side heat-sink are shown in the schematic. The electronics section includes a mix of analog circuit elements, including an inductor, 1.0 F super-capacitor, LDO regulator and a periodically switched load resistor. It also includes abstract or "math block" models to represent the state-average (non-switching) behavior of a buck-boost converter.

The goal of the design is to extract sufficient power from the TEG, to provide a 2.5-Watt/1-second duration power burst once every 10 seconds. This burst is presumably to supply power for a periodic data transmission. The simple MPPT algorithm that helps achieves this is visible in the open-source MPPT-TEG model shown. The MPPT algorithm dynamically adjusts the load current draw from the TEG, to keep it operating at its maximum power output capability. That capability varies with the differential operating temperature. That shift can more easily be seen in the followingTEC/TEG calibration test schematic:

https://www.systemvision.com/design/calibrate-tecteg-energy-harvesting

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Electrothermal Energy Harvesting - MPPT Capacitor Charging - on Sat, 10/03/2020 - 12:20

lucas.camacho.2018.br
Designer235362

lucas.camacho.2018.br

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4 years 2 months

9

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4

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Description

This example is intended to show relevant modeling and simulation capabilities of SystemVision Cloud for Electrothermal 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 example also illustrates using a sampled-data algorithm for maximum power point tracking (MPPT), to optimize the energy harvest for changing operating temperatures.

The design includes a thermoelectric generator (TEG) that is supplied on the "hot" side by a sinusoidally time varying temperature between 75 degC and 100 degC. The "cold" side is held at a fixed 25 degC. The thermal resistance and heat capacitance of the hot-side heat-sink are shown in the schematic. The electronics section includes a mix of analog circuit elements, including an inductor, 1.0 F super-capacitor, LDO regulator and a periodically switched load resistor. It also includes abstract or "math block" models to represent the state-average (non-switching) behavior of a buck-boost converter.

The goal of the design is to extract sufficient power from the TEG, to provide a 2.5-Watt/1-second duration power burst once every 10 seconds. This burst is presumably to supply power for a periodic data transmission. The simple MPPT algorithm that helps achieves this is visible in the open-source MPPT-TEG model shown. The MPPT algorithm dynamically adjusts the load current draw from the TEG, to keep it operating at its maximum power output capability. That capability varies with the differential operating temperature. That shift can more easily be seen in the followingTEC/TEG calibration test schematic:

https://www.systemvision.com/design/calibrate-tecteg-energy-harvesting

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Copy of Electrodynamic Vibration Energy Harvesting for IoT/IIoT - with State-Average Boost Converter - on Mon, 03/02/2020 - 17:21

brent_buescher
Designer230051

brent_buescher

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4 years 9 months

1

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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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ePower_916

ebookaktiv
Designer152721

ebookaktiv

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7 years 2 months

109

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Description

This example is intended to show relevant modeling and simulation capabilities of SystemVision Cloud, for kinetic 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 design contains mechanical, magnetic and electronic circuit elements, with energy conservation and cross-discipline dynamic interactions automatically included in the system model. The user can directly specify the physical or behavioral characteristics of many of the components. This includes the mass of the armature, the stiffness of the resonant spring, the cross sectional area of the magnetic core, the number of winding turns, the resistor and capacitor values, as well as the drop-out voltage of the linear regulator.In the nominal simulation results displayed on the schematic, the upper right waveform viewer shows the initial start-up of the system, with the amplitude of the external vibration source of 0.07 mm peak at 60 Hz, equivalent to a peak acceleration of 1 g. The nominal armature spring-mass resonance frequency is 60 Hz, and the armature displacement is seen to reach the frame's travel limit of 10 mm peak-to-peak!In the upper left, the waveform viewer is zoomed-in near the 1 sec. simulation time mark. It shows the time-varying core flux density and winding voltage, as the two air-gaps expand and contract with armature displacement, rapidly changing the magnetic flux path's effective reluctance value. In the lower right waveform viewer, the DC output voltage from the Schottky diode full-wave rectifier and the linear regulator output voltage are observed. The periodic disturbance is caused by the switched load being applied to the system.

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ePower_970

ebookaktiv
Designer152721

ebookaktiv

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7 years 2 months

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

Steven_2
Designer202382

Steven_2

Member for

6 years 3 months

2

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

KDAS
Designer197889

KDAS

Member for

6 years 8 months

8

designs

1

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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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Electrothermal Energy Harvesting - MPPT Capacitor Charging

Ilmir
Designer197998

Ilmir

Member for

6 years 7 months

5

designs

1

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Description

This example is intended to show relevant modeling and simulation capabilities of SystemVision Cloud for Electrothermal 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 example also illustrates using a sampled-data algorithm for maximum power point tracking (MPPT), to optimize the energy harvest for changing operating temperatures.The design includes a thermoelectric generator (TEG) that is supplied on the "hot" side by a sinusoidally time varying temperature between 75 degC and 100 degC. The "cold" side is held at a fixed 25 degC. The thermal resistance and heat capacitance of the hot-side heat-sink are shown in the schematic. The electronics section includes a mix of analog circuit elements, including an inductor, 1.0 F super-capacitor, LDO regulator and a periodically switched load resistor. It also includes abstract or "math block" models to represent the state-average (non-switching) behavior of a buck-boost converter.The goal of the design is to extract sufficient power from the TEG, to provide a 2.5-Watt/1-second duration power burst once every 10 seconds. This burst is presumably to supply power for a periodic data transmission. The simple MPPT algorithm that helps achieves this is visible in the open-source MPPT-TEG model shown. The MPPT algorithm dynamically adjusts the load current draw from the TEG, to keep it operating at its maximum power output capability. That capability varies with the differential operating temperature. That shift can more easily be seen in the followingTEC/TEG calibration test schematic:https://www.systemvision.com/design/calibrate-tecteg-energy-harvesting

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Live Electrothermal Energy Harvesting

Mike Donnelly
Designer19

Mike Donnelly

Member for

11 years 1 month

1,706

designs

10

groups

Member of the PartQuest Explore Development Team. Focused on modeling and simulation of analog, mixed-signal and multi-discipline systems covering a broad range of applications, including power electronics, controls and mechatronic systems.

Description

This example is intended to show relevant modeling and simulation capabilities of SystemVision Cloud for Electrothermal 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 example also illustrates using a sampled-data algorithm for maximum power point tracking (MPPT), to optimize the energy harvest for changing operating temperatures.The design includes a thermoelectric generator (TEG) that is supplied on the "hot" side by a sinusoidally time varying temperature between 75 degC and 100 degC. The "cold" side is held at a fixed 25 degC. The thermal resistance and heat capacitance of the hot-side heat-sink are shown in the schematic. The electronics section includes a mix of analog circuit elements, including an inductor, 1.0 F super-capacitor, LDO regulator and a periodically switched load resistor. It also includes abstract or "math block" models to represent the state-average (non-switching) behavior of a buck-boost converter.The goal of the design is to extract sufficient power from the TEG, to provide a 2.5-Watt/1-second duration power burst once every 10 seconds. This burst is presumably to supply power for a periodic data transmission. The simple MPPT algorithm that helps achieves this is visible in the open-source MPPT-TEG model shown. The MPPT algorithm dynamically adjusts the load current draw from the TEG, to keep it operating at its maximum power output capability. That capability varies with the differential operating temperature. That shift can more easily be seen in the followingTEC/TEG calibration test schematic:https://www.systemvision.com/design/calibrate-tecteg-energy-harvesting

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karthik20041999
Designer235586

lucas.camacho.2018.br
Designer235362

lucas.camacho.2018.br
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brent_buescher
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ebookaktiv
Designer152721

ebookaktiv
Designer152721

Steven_2
Designer202382

KDAS
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Ilmir
Designer197998

Mike Donnelly
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