PartQuest Explore Promote

groups

Add a bio to your profile to share information about yourself with other SystemVision users.

ePower_970

Designer152721

Member for

7 years 6 months

109

designs

groups

Add a bio to your profile to share information about yourself with other SystemVision users.

Solar Energy Harvesting for Battery Charging

Designer19

Member for

11 years 5 months

designs

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 circuit is an implementation of a buck converter power stage that could be used in the MPPT solar battery charger reference design found here: https://www.systemvision.com/design/solar-energy-harvesting-compare-direct-vs-mppt-battery-chargingThat reference example is appropriate for "system level" trade-off assessment, where the MPPT (maximum power point tracking) approach can be compared with direct solar panel battery charging. It uses a state-average (non-switching) buck converter model for much faster simulation, so that power tracking performance over long time periods can be observed. However, it does not account for some important aspects of a real converter implementation, such as the predicted power loss in the switching components, or the switching noise on the current and voltage measurements that are needed for power detection.This circuit confirms the performance of the system at full solar panel irradiance, and with the converter duty-cycle set to a fixed value of 0.8. That value was shown to provide maximum power under full irradiance. The results with this design confirm those of the reference design: The steady-state panel power output is just under 60 Watts (light blue waveform); The corresponding voltage (16 V) and current (3.7 A) into the converter (magenta and green waveforms, respectively); The current into the battery is 4.5 A (red waveform), as expected. Note also that the input L-C filter removes most of the switching noise that would be visible on these signals without that filtering effect. The average power loss in the P-channel MOSFET is 1.3 Watts (brown waveform). This is important for assessing overall system efficiency, a key factor in the trade-off analysis with direct charging. The user can move the waveform probes around and observe the power loss in all of the other components too, as part of the efficiency analysis process.

Solar Energy Harvesting - Compare Direct vs. MPPT Battery Charging

Designer197889

Member for

7 years

8

designs

groups

Add a bio to your profile to share information about yourself with other SystemVision users.

Description

This battery charging example compares direct solar battery charging vs. an MPPT algorithm combined with a buck 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 contains a few passive analog circuit elements, but 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 buck 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. 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 clear improvement in both panel power output (magenta vs. light blue waveforms) and battery input or charging current (red vs. dark blue waveforms), for MPPT vs. direct charging, respectively. Also, the actual duty-cycle "hunting" or peak power tracking operation is visible in green waveform, as the irradiance level varies sinusoidally (brown waveform).A companion design is available, which shows a circuit implementation of the buck converter power stage. That design can be used to analyze the efficiency of the MPPT approach. Efficiency can be a key factor in the trade-off assessment vs. simple direct charging. That circuit design can be found here: https://www.systemvision.com/design/solar-energy-harvesting-implementation-mppt-battery-charging

Solar Energy Harvesting - Implementation of MPPT Battery Charging

Designer197889

Member for

7 years

8

designs

groups

Add a bio to your profile to share information about yourself with other SystemVision users.

Description

This circuit is an implementation of a buck converter power stage that could be used in the MPPT solar battery charger reference design found here: https://www.systemvision.com/design/solar-energy-harvesting-compare-direct-vs-mppt-battery-chargingThat reference example is appropriate for "system level" trade-off assessment, where the MPPT (maximum power point tracking) approach can be compared with direct solar panel battery charging. It uses a state-average (non-switching) buck converter model for much faster simulation, so that power tracking performance over long time periods can be observed. However, it does not account for some important aspects of a real converter implementation, such as the predicted power loss in the switching components, or the switching noise on the current and voltage measurements that are needed for power detection.This circuit confirms the performance of the system at full solar panel irradiance, and with the converter duty-cycle set to a fixed value of 0.8. That value was shown to provide maximum power under full irradiance. The results with this design confirm those of the reference design: The steady-state panel power output is just under 60 Watts (light blue waveform); The corresponding voltage (16 V) and current (3.7 A) into the converter (magenta and green waveforms, respectively); The current into the battery is 4.5 A (red waveform), as expected. Note also that the input L-C filter removes most of the switching noise that would be visible on these signals without that filtering effect. The average power loss in the P-channel MOSFET is 1.3 Watts (brown waveform). This is important for assessing overall system efficiency, a key factor in the trade-off analysis with direct charging. The user can move the waveform probes around and observe the power loss in all of the other components too, as part of the efficiency analysis process.

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

Designer197889

Member for

7 years

8

designs

groups

Add a bio to your profile to share information about yourself with other SystemVision users.

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.

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

Designer19

Member for

11 years 5 months

designs

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 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.

Example_920

Designer152721

Member for

7 years 6 months

109

designs

groups

Add a bio to your profile to share information about yourself with other SystemVision users.

Solar Energy Harvesting - Implementation of MPPT Battery Charging

Designer19

Member for

11 years 5 months

designs

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 circuit is an implementation of a buck converter power stage that could be used in the MPPT solar battery charger reference design found here:

https://explore.partquest.com/node/661856

That reference example is appropriate for "system level" trade-off assessment, where the MPPT (maximum power point tracking) approach can be compared with direct solar panel battery charging. It uses a state-average (non-switching) buck converter model for much faster simulation, so that power tracking performance over long time periods can be observed. However, it does not account for some important aspects of a real converter implementation, such as the predicted power loss in the switching components, or the switching noise on the current and voltage measurements that are needed for power detection.

This circuit confirms the performance of the system at full solar panel irradiance, and with the converter duty-cycle set to a fixed value of 0.8. That value was shown to provide maximum power under full irradiance. The results with this design confirm those of the reference design: The steady-state panel power output is just under 60 Watts (light blue waveform); The corresponding voltage (16.7 V) and current (3.58 A) into the converter (magenta and green waveforms, respectively); The current into the battery is 4.45 A (red waveform), as expected. Note also that the input L-C filter removes most of the switching noise that would be visible on these signals without that filtering effect.

The average power loss in the P-channel MOSFET is just under 1.8 Watts (brown waveform). This is important for assessing overall system efficiency, a key factor in the trade-off analysis with direct charging. The user can move the waveform probes around and observe the power loss in all of the other components too, as part of the efficiency analysis process.

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

Designer19

Member for

11 years 5 months

designs

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 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.