𝐀𝐍𝐍 𝐁𝐚𝐬𝐞𝐝 𝐌𝐏𝐏𝐓 𝐟𝐨𝐫 𝐒𝐨𝐥𝐚𝐫 𝐏𝐕 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐁𝐋𝐃𝐂 𝐌𝐨𝐭𝐨𝐫

𝐀𝐍𝐍 𝐁𝐚𝐬𝐞𝐝 𝐌𝐏𝐏𝐓 𝐟𝐨𝐫 𝐒𝐨𝐥𝐚𝐫 𝐏𝐕 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐁𝐋𝐃𝐂 𝐌𝐨𝐭𝐨𝐫

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧

Solar-powered motor-drive systems offer a clean and efficient solution for applications requiring reliable electrical energy and controlled motor operation.

𝐀𝐍𝐍 𝐁𝐚𝐬𝐞𝐝 𝐌𝐏𝐏𝐓 𝐟𝐨𝐫 𝐒𝐨𝐥𝐚𝐫 𝐏𝐕 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐁𝐋𝐃𝐂 𝐌𝐨𝐭𝐨𝐫

ANN Based MPPT for Solar PV battery Powered BLDC Motor

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This MATLAB/Simulink model presents an ANN-based maximum power point tracking system for operating a 24 V BLDC motor using:

• A solar PV array

• An artificial neural network MPPT controller

• A boost converter

• A 12 V battery

• A bidirectional DC–DC converter

• A voltage-regulated DC link

• A voltage-source inverter

• A BLDC motor drive

The artificial neural network MPPT controller extracts maximum available power from the PV array under changing solar irradiance and temperature conditions.

The battery supports the motor when the available PV power decreases and absorbs surplus solar power when PV generation exceeds the motor requirement.

𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰

The system is designed around a 24 V DC bus and a small 24 V BLDC motor.

The complete power flow is:

Solar PV Array → Boost Converter → 24 V DC Bus → Inverter → BLDC Motor

The battery is connected to the DC bus through a bidirectional DC–DC converter.

Main system sections

Section	Main function
Solar PV array	Generates electrical power from solar energy
ANN MPPT controller	Determines the optimum PV operating voltage
Boost converter	Raises the PV voltage to the required DC-bus level
Battery storage	Stores surplus energy and supplies deficit power
Bidirectional converter	Controls battery charging and discharging
DC bus	Maintains the required 24 V supply
Voltage-source inverter	Converts DC power into the required motor supply
BLDC motor	Converts electrical energy into mechanical output
Gate decoder	Produces inverter switching signals using rotor-position information

𝐁𝐋𝐃𝐂 𝐌𝐨𝐭𝐨𝐫 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬

The motor is configured using standard manufacturer specifications. A trapezoidal back-EMF model is selected in the BLDC motor block.

Parameter	Value
Rated voltage	24 V
Rated power	Approximately 39–40 W
Rated speed	3000 rpm
Rated torque	0.125 N·m
Rated current	Approximately 2.5 A
Peak current	7.5 A
Peak torque	0.375 N·m
Torque constant	0.05 N·m/A
Line-to-line resistance	1.35 Ω
Number of poles	8
Number of pole pairs	4
Moment of inertia	0.33 kg·cm²
Back-EMF type	Trapezoidal

After computing the block parameters from the manufacturer data, the model reports:

Computed parameter	Value
Stator phase resistance	0.675 Ω
Stator phase inductance	0.0045
Computed voltage-related parameter	Approximately 0.236

These parameters are applied directly to the BLDC motor model in MATLAB/Simulink.

𝐒𝐨𝐥𝐚𝐫 𝐏𝐕 𝐀𝐫𝐫𝐚𝐲 𝐂𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧

The PV array is selected with a power rating higher than the BLDC motor rating. This allows the PV system to operate the motor and charge the battery when sufficient solar energy is available.

PV parameter	Value
Approximate single-panel power	49 W
Number of parallel panels	2
Total PV power	Approximately 100 W
Voltage at maximum power point	17 V
Current at maximum power point per panel	2.941 A
Approximate total PV current	5.8 A
Required DC-bus voltage	24 V

Connecting two panels in parallel maintains the PV voltage at approximately 17 V while increasing the available current and total power.

𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐂𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧

The battery provides energy storage and supports the BLDC motor during low solar-power conditions.

Battery parameter	Value
Nominal voltage	12 V
Capacity	100 Ah
Converter power rating	Approximately 100 W
DC-bus voltage	24 V
Initial state of charge	Approximately 50%
Operating modes	Charging and discharging

Because the battery voltage is lower than the DC-bus voltage, a bidirectional converter is used to control power exchange between the battery and the DC bus.

𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬

1. Solar-power generation

The PV array produces power according to the applied irradiance and temperature.

• Higher irradiance produces more PV current and power.

• Lower irradiance reduces the available solar power.

• The PV voltage is controlled near its optimum operating point.

2. ANN-based maximum power point tracking

The neural network receives two main inputs:

• Solar irradiance

• PV temperature

The trained ANN produces the required reference PV voltage.

This reference voltage is compared with the measured PV voltage. The voltage error is then processed by a PI controller.

3. Boost-converter operation

The PI controller generates the required duty-cycle command.

The duty cycle is supplied to a PWM generator, which produces the switching pulse for the boost-converter IGBT.

The boost converter:

• Raises the PV voltage from approximately 17 V

• Supplies power to the 24 V DC bus

• Maintains the PV array near its maximum-power operating point

4. DC-bus voltage regulation

The DC-bus voltage is measured and compared with the 24 V reference.

The voltage error is processed by a PI controller. Its output controls the PWM pulses of the bidirectional converter.

This control maintains the inverter input voltage close to 24 V even when solar irradiance changes.

5. BLDC motor operation

The regulated DC voltage is supplied to the inverter.

The inverter and gate-decoder system energize the BLDC motor phases according to the rotor-position signals.

The motor operates at the specified load torque while the model monitors:

• Rotor speed

• Electromagnetic torque

• Stator current

• Back EMF

• Inverter input power

• DC-link voltage

𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲

The system uses two major control loops.

ANN MPPT control

Control stage	Description
Inputs	Solar irradiance and temperature
ANN output	Reference PV voltage
Feedback signal	Actual PV voltage
Error controller	PI controller
Modulation	PWM generator
Controlled converter	PV boost converter
Main objective	Maximum PV power extraction

The ANN learns the relationship between environmental conditions and the optimum PV voltage.

Compared with a fixed operating point, the ANN controller can adjust the PV reference as irradiance and temperature change.

Battery-converter control

Control stage	Description
Reference value	24 V
Feedback signal	Measured DC-bus voltage
Error controller	PI controller
Modulation	PWM generator
Controlled converter	Bidirectional DC–DC converter
Main objective	DC-bus voltage regulation
Secondary function	Battery charging and discharging

The bidirectional converter automatically changes the direction of battery power depending on the available PV power and motor demand.

𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐏𝐨𝐰𝐞𝐫-𝐅𝐥𝐨𝐰 𝐋𝐨𝐠𝐢𝐜

The BLDC motor requires approximately 40 W at its rated operating point. The PV array can produce nearly 100 W under high irradiance.

When PV power is higher than the motor requirement

• The PV array operates the BLDC motor.

• Excess PV power is transferred to the battery.

• Battery current becomes negative according to the model sign convention.

• Battery-converter power becomes negative.

• Battery state of charge increases.

When PV power is lower than the motor requirement

• The PV array continues supplying available power.

• The battery supplies the remaining required power.

• Battery current becomes positive.

• Battery-converter power becomes positive.

• Battery state of charge decreases.

• The DC-bus voltage remains close to 24 V.

Power-flow interpretation

Operating condition	Battery mode	Battery-current sign	Battery-power sign
Surplus PV power	Charging	Negative	Negative
Insufficient PV power	Discharging	Positive	Positive

𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬

The model is tested under changing irradiance conditions.

Simulation parameter	Applied value
Irradiance profile	1000, 600, 400 and 600 W/m²
Irradiance-changing interval	Approximately every 1 second
Motor load torque	0.125 N·m
DC-bus reference	24 V
Initial battery state of charge	Approximately 50%
Motor rated speed	3000 rpm

The results include measurements of:

• PV voltage

• PV current

• PV power

• PV-converter output power

• Battery voltage

• Battery current

• Battery state of charge

• Battery-converter power

• Load power

• DC-bus voltage

• Rotor speed

• Electromagnetic torque

• Stator current

• Back EMF

𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬

Operation at high irradiance

At approximately 1000 W/m²:

Observed quantity	Approximate result
PV voltage	17 V
PV current	5.8–5.9 A
PV power	Approximately 95–100 W
Battery current	Approximately −2 A
Battery-converter power	Approximately −35 W
Inverter input or load power	Approximately 60 W
DC-bus voltage	Approximately 24 V
Battery operating mode	Charging
Battery SoC	Increases from approximately 50%

The negative battery current indicates that surplus PV power is being stored in the battery.

The PV array supplies the motor load while the remaining power charges the battery.

Operation at reduced irradiance

When irradiance decreases from 1000 W/m² to approximately 600 W/m²:

Observed quantity	Approximate result
PV power	Approximately 60 W
Reduction in PV power	Approximately 40 W
Battery current	Changes from negative to nearly +1 A
Battery-converter power	Changes from negative to positive
Battery operating mode	Discharging
DC-bus voltage	Maintained near 24 V
Motor operation	Continues without interruption

The reduction in PV power causes the battery to change from charging mode to discharging mode.

The battery provides the additional power required by the motor and helps maintain the DC-bus voltage.

Battery state-of-charge response

The battery SoC initially increases from approximately:

• 50% to about 50.006% during charging

When solar power decreases:

• The SoC begins to fall

• Battery current becomes positive

• Battery power changes from negative to positive

This confirms the correct operation of the bidirectional power-flow control.

BLDC motor response

The simulated motor results demonstrate:

• Smooth speed development after startup

• Stable rotor-speed operation

• Torque production near the applied load requirement

• Trapezoidal back-EMF characteristics

• Controlled stator-current waveform

• Continuous operation during irradiance changes

The solar and battery power sources work together to prevent interruption of the motor supply.

𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬

• ANN-based MPPT using irradiance and temperature inputs

• Maximum-power extraction under changing solar conditions

• Approximately 100 W solar PV array

• 24 V, 40 W BLDC motor

• 12 V, 100 Ah battery

• PV boost-converter control

• Battery bidirectional-converter control

• Automatic battery charging and discharging

• Regulated 24 V DC bus

• PI-based voltage-control loops

• PWM-controlled power converters

• Motor speed, torque, current and back-EMF monitoring

• PV, battery and load-power measurement

• Dynamic simulation under multiple irradiance levels

𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 𝐨𝐟 𝐀𝐍𝐍-𝐁𝐚𝐬𝐞𝐝 𝐌𝐏𝐏𝐓

Adaptive tracking

The ANN estimates the optimum PV reference voltage according to environmental conditions.

Improved PV utilization

The controller attempts to extract the maximum available power instead of operating the PV array at a fixed voltage.

Fast reference generation

Once trained, the neural network can directly generate the optimum voltage reference from the irradiance and temperature inputs.

Reliable motor operation

The coordinated PV and battery control maintains the motor power supply during variations in solar generation.

Effective energy management

The battery automatically absorbs surplus energy and supplies deficit power without interrupting the BLDC motor.

𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

This solar PV battery-powered BLDC motor system can be studied for applications such as:

• Solar-powered ventilation systems

• Small water-pumping systems

• Agricultural motor drives

• Portable solar-powered machines

• Battery-assisted renewable-energy systems

• Low-voltage DC motor drives

• Standalone solar systems

• Energy-efficient cooling systems

• Solar-powered laboratory prototypes

• Intelligent power-electronics control studies

𝐖𝐡𝐲 𝐔𝐬𝐞 𝐚 𝐁𝐋𝐃𝐂 𝐌𝐨𝐭𝐨𝐫?

BLDC motors are suitable for renewable-energy applications because they provide:

• High operating efficiency

• Compact construction

• High power-to-weight ratio

• Low maintenance requirements

• Good speed response

• Reliable electronic commutation

• Reduced mechanical wear

• Efficient low-voltage operation

The absence of mechanical brushes improves reliability and makes the motor suitable for battery-powered systems.

𝐖𝐡𝐲 𝐈𝐬 𝐚 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐝?

Solar PV power changes with irradiance and temperature. Without energy storage, a sudden reduction in solar power can affect the DC-bus voltage and motor performance.

The battery helps to:

• Store unused PV energy

• Supply power during low irradiance

• Support the DC-link voltage

• Reduce power fluctuations

• Maintain continuous motor operation

• Improve overall system reliability

𝐅𝐫𝐞𝐪𝐮𝐞𝐧𝐭𝐥𝐲 𝐀𝐬𝐤𝐞𝐝 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

What are the inputs of the ANN MPPT controller?

The ANN uses solar irradiance and temperature as its inputs.

What is the output of the ANN?

The network generates the optimum PV reference voltage.

Why is a boost converter used?

The PV maximum-power voltage is approximately 17 V, while the required DC-bus voltage is 24 V. The boost converter raises the PV voltage to the required level.

Why is a bidirectional converter used for the battery?

It allows power to flow in both directions:

• From the DC bus to the battery during charging

• From the battery to the DC bus during discharging

How is the DC-bus voltage controlled?

The measured DC-bus voltage is compared with the 24 V reference. A PI controller and PWM generator control the battery converter to regulate the voltage.

What happens when PV power decreases?

The battery changes to discharging mode and supplies the power deficit while the PV array continues providing its available power.

What indicates battery charging in the results?

According to the model sign convention, negative battery current and negative battery-converter power indicate charging.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧

The ANN Based MPPT for Solar PV Battery Powered BLDC Motor model demonstrates intelligent coordination among a solar PV array, battery-storage system, power converters and a BLDC motor drive.

The ANN MPPT controller uses irradiance and temperature to determine the optimum PV voltage reference. The boost converter then operates the PV array near its maximum-power point and supplies energy to the 24 V DC bus.

During high solar irradiance, the PV array operates the motor and charges the battery using the surplus power. When irradiance decreases, the bidirectional converter changes the battery to discharging mode and supplies the required additional power.

The simulation results show:

• Maximum-power extraction from the PV array

• Automatic battery charging and discharging

• DC-bus voltage regulation near 24 V

• Continuous BLDC motor operation

• Stable speed and torque response under changing irradiance

This MATLAB/Simulink model provides a clear platform for understanding ANN MPPT control, solar PV energy conversion, battery energy management, bidirectional converter operation and BLDC motor-drive performance.