Solar PV Battery Powered Electric Vehicle in MATLAB
Solar PV Battery Powered Electric Vehicle in MATLAB
𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧
This MATLAB/Simulink model demonstrates a solar PV and battery-powered electric vehicle with renewable energy generation, battery energy storage, bidirectional power flow, Incremental Conductance MPPT, DC-bus voltage regulation, and a BLDC motor drive.
The system is designed to show how solar energy and battery storage can work together to supply an electric vehicle under changing solar irradiation and driving conditions.
Electric vehicles can significantly benefit from renewable energy when solar PV generation is combined with battery energy storage.
In this MATLAB/Simulink model:
☀️ A 2 kW Solar PV Array generates renewable power.
⚡ A boost converter raises the PV voltage to the required DC-bus level.
📈 Incremental Conductance MPPT extracts maximum available PV power.
🔋 A battery energy storage system supports the DC bus.
🔄 A bidirectional DC/DC converter manages battery charging and discharging.
🚗 A BLDC motor drive represents the electric vehicle traction system.
💡 Additional electrical loads represent auxiliary EV loads.
🎯 The common DC-bus voltage is regulated around 400 V.
The simulation makes it easy to study solar power variation, battery power sharing, EV load demand, motor-speed response, and battery SOC.
𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰
The complete Solar PV Battery Powered Electric Vehicle model consists of several interconnected sections.
System Section | Main Function |
Solar PV Array | Generates electrical energy from solar irradiation |
Boost Converter | Steps PV voltage up to the DC-bus voltage |
Incremental Conductance MPPT | Extracts maximum available PV power |
DC Bus | Common energy link for PV, battery, and EV |
Battery Storage | Stores excess energy and supplies deficit power |
Bidirectional Converter | Controls charging and discharging |
Battery Controller | Regulates DC-bus voltage |
Voltage Source Inverter | Supplies controlled power to the BLDC motor |
BLDC Motor | Provides traction for the electric vehicle |
Speed Controller | Makes motor speed follow the drive-cycle reference |
EV Auxiliary Load | Represents additional vehicle electrical demand |
Measurement System | Measures voltage, current, power, speed, and SOC |
𝐒𝐨𝐥𝐚𝐫 𝐏𝐕 𝐒𝐲𝐬𝐭𝐞𝐦
The solar PV source is rated at approximately 2000 W.
PV Array Parameters
Parameter | Value |
Total PV power | 2000 W |
Single-panel power | 250 W |
Number of panels | 8 |
Connection | Series |
Maximum-power voltage per panel | 30.7 V |
Maximum-power current | 8.15 A |
Approximate array operating voltage | 245 V |
DC-bus reference voltage | 400 V |
Eight 250 W PV modules are connected in series to obtain the required array rating.
Because the PV array voltage is lower than the required DC-bus voltage, a DC/DC boost converter interfaces the PV source with the common DC link.
𝐈𝐧𝐜𝐫𝐞𝐦𝐞𝐧𝐭𝐚𝐥 𝐂𝐨𝐧𝐝𝐮𝐜𝐭𝐚𝐧𝐜𝐞 𝐌𝐏𝐏𝐓
Solar PV output continuously changes when irradiation and temperature change.
Therefore, operating the PV array at a fixed voltage cannot guarantee maximum power extraction.
The model uses an Incremental Conductance MPPT algorithm.
MPPT Controller Inputs and Output
Signal | Purpose |
PV Voltage | Identifies PV operating condition |
PV Current | Determines available PV power condition |
Duty Cycle | Controls the boost converter |
The MPPT controller continuously observes the PV voltage and current and adjusts the converter duty cycle.
The resulting duty-cycle command is sent to a PWM generator, which produces switching pulses for the boost-converter IGBT.
This allows the system to:
Track changing PV operating conditions.
Extract the maximum available solar power.
Maintain efficient converter operation.
Adapt automatically to changing irradiation.
𝐏𝐕 𝐈𝐫𝐫𝐚𝐝𝐢𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬
PV characteristics can be observed under different solar irradiation levels at approximately 25 °C.
Irradiation | Expected Effect |
1000 W/m² | Highest PV power |
800 W/m² | Reduced PV power |
600 W/m² | Further reduction in available power |
Low PV generation |
As irradiation decreases, the PV maximum-power point shifts. The Incremental Conductance algorithm follows this changing operating point.
For the dynamic simulation, a wider irradiation variation is applied.
Dynamic Irradiation Profile
Simulation Interval | Irradiation Level |
Initial condition | 1000 W/m² |
Next step | 800 W/m² |
Next step | 500 W/m² |
Next step | 300 W/m² |
Final step | 100 W/m² |
The irradiation is changed approximately every 1 second, allowing the performance of the PV source, MPPT controller, battery, and EV load to be analyzed under changing solar conditions.
𝐁𝐨𝐨𝐬𝐭 𝐂𝐨𝐧𝐯𝐞𝐫𝐭𝐞𝐫
The PV array produces approximately 245 V, while the electric vehicle system uses a DC bus regulated around 400 V.
Therefore, the boost converter performs two important functions:
Increases the PV-side voltage to the required DC-link level.
Controls the PV operating point according to the MPPT command.
The converter switching device receives PWM pulses generated from the Incremental Conductance MPPT duty-cycle command.
This arrangement enables both voltage boosting and maximum-power extraction.
𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐄𝐧𝐞𝐫𝐠𝐲 𝐒𝐭𝐨𝐫𝐚𝐠𝐞
A battery storage system is connected to the same DC bus through a bidirectional DC/DC converter.
Unlike a conventional unidirectional converter, the bidirectional converter can transfer energy in both directions.
Battery Operating Modes
Operating Condition | Battery Action | Battery Power |
PV power exceeds EV demand | Charging | Negative |
PV power approximately equals demand | Low power exchange | Near zero |
PV power is below EV demand | Discharging | Positive |
This allows the battery to automatically compensate for solar-power fluctuations.
When solar generation is high, excess energy can be stored.
When solar generation decreases, stored battery energy is supplied to the EV.
𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐂𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐫
The battery converter is controlled using DC-bus voltage regulation.
The required DC-link voltage is approximately:
Parameter | Value |
DC-bus reference | 400 V |
Controlled variable | DC-bus voltage |
Main controller | PI Controller |
Converter | Bidirectional DC/DC |
Switching control | PWM |
The measured DC-bus voltage is compared with the reference value.
The resulting control signal is processed through the PI controller and PWM generation stage.
Based on the system power balance, the converter changes the direction and magnitude of battery current.
This helps maintain a nearly constant 400 V DC bus even when PV generation and EV demand change.
𝐄𝐥𝐞𝐜𝐭𝐫𝐢𝐜 𝐕𝐞𝐡𝐢𝐜𝐥𝐞 𝐰𝐢𝐭𝐡 𝐁𝐋𝐃𝐂 𝐌𝐨𝐭𝐨𝐫 𝐃𝐫𝐢𝐯𝐞
The electric vehicle section contains a BLDC motor drive and additional electrical load.
The BLDC traction system includes:
Voltage Source Inverter.
BLDC motor.
Hall-sensor signals.
Closed-loop speed controller.
PI speed controller.
PWM generator.
Switching logic.
Drive-cycle speed reference.
The motor converts electrical energy from the DC bus into mechanical energy for vehicle propulsion.
𝐁𝐋𝐃𝐂 𝐌𝐨𝐭𝐨𝐫 𝐒𝐩𝐞𝐞𝐝 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
The actual BLDC motor speed is continuously measured and compared with the desired drive-cycle speed.
The speed error is processed by a PI controller.
The controller determines the required PWM duty cycle.
The Hall-sensor-based commutation signals and PWM signals are combined to generate appropriate switching commands for the voltage source inverter.
This allows the inverter to control:
Motor input power.
Motor phase currents.
Electromagnetic torque.
Motor acceleration.
Steady-state speed.
Deceleration.
A load torque of approximately 3 N·m is applied to represent the mechanical load on the electric vehicle drive.
𝐃𝐫𝐢𝐯𝐞 𝐂𝐲𝐜𝐥𝐞
The simulation uses a simple speed profile to evaluate the dynamic performance of the EV drive.
Time | Speed Reference | Vehicle Condition |
0–1.5 s | 0 → 2000 RPM | Acceleration |
1.5–3.5 s | 2000 RPM | Constant-speed operation |
3.5–5 s | 2000 → 0 RPM | Deceleration |
This drive cycle allows the controller to be tested under three important operating conditions.
Acceleration
The motor speed increases from zero to 2000 RPM.
The motor requires increased electrical power and torque during this period.
Constant Speed
The motor maintains approximately 2000 RPM.
The speed controller adjusts inverter operation to compensate for the applied load.
Deceleration
The speed reference gradually decreases.
The motor controller reduces the motor speed according to the commanded drive cycle.
𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬
The complete energy-flow process can be understood in a few steps:
Solar irradiation reaches the PV array.
The PV array generates DC voltage and current.
Incremental Conductance MPPT identifies the required operating condition.
The boost converter raises the PV voltage toward the 400 V DC bus.
PV energy is supplied to the EV load.
Excess PV generation charges the battery.
Reduced PV generation causes the battery to discharge.
The bidirectional converter regulates battery power exchange.
The voltage source inverter supplies the BLDC motor.
Closed-loop control makes the motor follow the drive-cycle speed command.
𝐏𝐨𝐰𝐞𝐫 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭
One of the most useful features of this simulation is automatic power sharing between the solar PV source and battery.
PV Condition | EV Demand | Battery Response |
High PV generation | Lower than PV output | Battery charges |
PV and load balanced | Similar | Minimal battery exchange |
Moderate PV generation | Greater than PV output | Battery discharges |
Very low irradiation | Mainly supplied by battery | Increased discharge |
This behavior ensures continuity of EV operation even when solar power changes rapidly.
𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭𝐬
The MATLAB/Simulink model monitors several important electrical and mechanical variables.
Measurement | Purpose |
PV Voltage | Observes PV operating voltage |
PV Current | Monitors generated current |
PV Power | Evaluates MPPT performance |
DC-Bus Voltage | Checks DC-link regulation |
EV Input Current | Measures EV current demand |
EV Input Power | Determines total EV power consumption |
Battery Voltage | Monitors battery terminal voltage |
Battery Current | Identifies charging/discharging |
Battery Power | Studies PV-battery power sharing |
Battery SOC | Tracks energy-storage condition |
Motor Speed | Evaluates drive-cycle tracking |
Motor Torque | Observes mechanical loading |
Stator Current | Examines BLDC electrical behavior |
Back EMF | Evaluates BLDC motor operation |
𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬
The simulation demonstrates stable operation under changing solar and vehicle conditions.
☀️ PV Power Response
As irradiation decreases from 1000 W/m² toward 100 W/m², available PV power also decreases.
The MPPT controller continuously adjusts the converter operating point to obtain the maximum possible power.
⚡ DC-Bus Voltage
Despite changing PV generation and EV demand, the DC bus remains close to its 400 V reference.
This confirms the effectiveness of the battery bidirectional converter and voltage-control loop.
🔋 Battery Charging
When PV generation is greater than EV demand:
Battery current changes toward charging operation.
Battery power becomes negative.
Battery SOC increases.
🔋 Battery Discharging
When PV generation becomes lower than the EV power demand:
The battery supplies the missing power.
Battery power becomes positive.
SOC gradually decreases.
🚗 Motor-Speed Response
The BLDC motor follows the drive-cycle command effectively:
Smooth acceleration toward 2000 RPM.
Stable operation around 2000 RPM.
Controlled reduction in speed during deceleration.
𝐊𝐞𝐲 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬
Parameter | Specification |
PV array power | 2000 W |
PV module rating | 250 W |
Number of PV modules | 8 |
PV module Vmpp | 30.7 V |
PV module Impp | 8.15 A |
Approximate PV array voltage | 245 V |
DC-bus voltage | 400 V |
MPPT method | Incremental Conductance |
Battery interface | Bidirectional DC/DC converter |
Battery control | DC-bus voltage regulation |
Motor | BLDC |
Motor controller | Closed-loop PI speed control |
Maximum drive-cycle speed | 2000 RPM |
Mechanical load torque | 3 N·m |
Simulation duration | 5 s |
Temperature | 25 °C |
Maximum irradiation | 1000 W/m² |
Minimum dynamic irradiation | 100 W/m² |
𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬
✅ Complete Solar PV–Battery EV architecture in MATLAB/Simulink.
✅ 2 kW photovoltaic power-generation system.
✅ Incremental Conductance MPPT implementation.
✅ PV boost-converter control.
✅ 400 V common DC-bus regulation.
✅ Bidirectional battery charging and discharging.
✅ Automatic PV-battery power sharing.
✅ BLDC motor-based electric vehicle traction.
✅ Closed-loop motor-speed control.
✅ Hall-sensor-based inverter commutation.
✅ Variable solar irradiation analysis.
✅ Acceleration, constant-speed, and deceleration testing.
✅ Battery voltage, current, power, and SOC monitoring.
✅ PV voltage, current, and power monitoring.
✅ EV input current and power analysis.
𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
This simulation is useful for studying:
Solar-assisted electric vehicle systems.
Renewable energy-based EV powertrains.
Battery energy management.
Bidirectional DC/DC converters.
DC microgrids for transportation.
MPPT control methods.
BLDC motor drives.
Power-sharing strategies.
Energy-storage integration.
EV traction-control systems.
Renewable charging architectures.
MATLAB/Simulink power-electronics modeling.
It is particularly useful for students, researchers, engineers, educators, and MATLAB/Simulink learners who want to understand the interaction between renewable generation, battery storage, and electric vehicle loads.
𝐖𝐡𝐚𝐭 𝐂𝐚𝐧 𝐁𝐞 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐅𝐫𝐨𝐦 𝐓𝐡𝐢𝐬 𝐌𝐨𝐝𝐞𝐥?
By studying this simulation, learners can understand:
How a PV array supplies an EV through a DC bus.
Why MPPT is required for variable solar conditions.
How a boost converter interfaces PV with a higher-voltage DC link.
How battery storage compensates for changing PV generation.
How a bidirectional converter enables charging and discharging.
How a PI controller regulates DC-bus voltage.
How a BLDC motor drive follows a speed reference.
How solar irradiation affects battery power sharing.
How SOC changes during charging and discharging.
How electrical and mechanical variables can be analyzed in Simulink.
𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧
The Solar PV Battery Powered Electric Vehicle in MATLAB demonstrates an integrated renewable-energy-based EV architecture combining solar PV generation, battery storage, power converters, MPPT control, and a BLDC motor drive.
The simulation shows that the Incremental Conductance MPPT successfully adapts the PV operating point to changing irradiation. The battery energy-storage system compensates for differences between solar generation and vehicle power demand, while the bidirectional converter maintains the DC bus close to 400 V.
During high solar generation, excess PV energy charges the battery. When solar generation becomes insufficient, the battery automatically discharges to support the EV.
The BLDC motor also follows the acceleration, constant-speed, and deceleration commands effectively.
Overall, the model provides a clear platform for understanding solar-powered electric vehicles, battery energy management, MPPT control, bidirectional converters, BLDC motor drives, and renewable energy integration using MATLAB/Simulink.
Comments