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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

Copy of Solar Energy Harvesting - Compare Direct vs. MPPT Battery Charging - on Fri, 10/16/2020 - 22:00

karthik20041999
Designer235586

karthik20041999

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4 years 1 month

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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

Copy of Solar Energy Harvesting - Compare Direct vs. MPPT Battery Charging - on Thu, 10/01/2020 - 22:32

mada84
Designer224203

mada84

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5 years 1 month

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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

State Average CM Boost Converter

Mike Donnelly
Designer19

Mike Donnelly

Member for

11 years

1,700

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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.

Copy of Solar Energy Harvesting - Compare Direct vs. MPPT Battery Charging - on Tue, 09/15/2020 - 10:46

mada84
Designer224203

mada84

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5 years 1 month

8

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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

Copy of Solar Energy Harvesting for Battery Charging - on Mon, 09/14/2020 - 12:29

a.koufalexis
Designer234745

a.koufalexis

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4 years 2 months

4

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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-charging

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 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.

Copy of Solar Energy Harvesting - Compare Direct vs. MPPT Battery Charging - on Tue, 09/01/2020 - 14:53

aungtun
Designer211422

aungtun

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5 years 10 months

9

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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

Copy of Solar Energy Harvesting - Compare Direct vs. MPPT Battery Charging - on Mon, 08/31/2020 - 00:19

Trident-Labs
Designer199613

Trident-Labs

Member for

6 years 5 months

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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

Copy of Solar Energy Harvesting - Compare Direct vs. MPPT Battery Charging - on Wed, 08/26/2020 - 07:59

tokoescobar
Designer234367

tokoescobar

Member for

4 years 3 months

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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

Copy of Solar Energy Harvesting - Implementation of MPPT Battery Charging - on Tue, 08/25/2020 - 14:30

mario.24medina
Designer232513

mario.24medina

Member for

4 years 5 months

18

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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-charging

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 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.