PV based EV Charging Station with Fuzzy MPPT in MATLAB
PV based EV Charging Station with Fuzzy MPPT in MATLAB
𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧
The PV based EV Charging Station with Fuzzy MPPT in MATLAB is a grid-connected electric vehicle charging system developed using MATLAB/Simulink.
The system combines:
Solar PV generation
Fuzzy Logic MPPT control
Boost converter
EV battery charging
Bidirectional DC–DC converter
Common DC bus
Grid-connected inverter
Current control in the dq reference frame
The main objective is to utilize available solar power for charging the EV battery while obtaining the remaining required charging power from the grid.
𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰
The complete charging station consists of three major energy sections:
Section | Main Function |
Solar PV | Generates renewable DC power |
EV Battery | Receives controlled charging power |
Utility Grid | Supports charging when PV power is insufficient |
Common DC Bus | Connects the PV, battery and inverter stages |
Grid Inverter | Controls power exchange between DC bus and grid |
Main power conversion stages
PV array → Boost converter → DC bus
EV battery → Bidirectional DC–DC converter → DC bus
Grid → Filter → Full-bridge inverter → DC bus
This configuration allows coordinated power flow between the PV source, EV battery and utility grid.
𝐏𝐕 𝐒𝐲𝐬𝐭𝐞𝐦 𝐚𝐧𝐝 𝐅𝐮𝐳𝐳𝐲 𝐌𝐏𝐏𝐓
The PV system is connected to the common DC bus through a boost DC–DC converter.
PV voltage and PV current are continuously measured. From these measurements, the controller evaluates the variation in PV operating conditions.
The Fuzzy MPPT controller uses information related to:
Change in PV voltage
Change in PV power
PV power-voltage operating slope
Change in the operating slope
These signals are supplied to the fuzzy controller to determine the required control action.
Fuzzy controller structure
The fuzzy MPPT contains:
Error input
Change of error input
Membership functions
Fuzzy rule base
Defuzzification stage
Duty-cycle output
The generated duty cycle is applied to the PWM generator controlling the PV boost converter switch.
This continuously shifts the PV operating point toward the maximum available solar power.
𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬
The system operates according to the available PV power and required EV charging power.
When PV generation is high
PV supplies most or all of the EV charging requirement.
Grid power requirement decreases.
The EV battery continues operating in charging mode.
When PV generation decreases
Available PV power becomes lower than the required charging power.
The grid supplies the remaining power.
Battery charging power is maintained close to the desired value.
When PV generation becomes zero
Solar contribution is unavailable.
The grid provides the required EV charging power.
Battery charging continues without interruption.
Therefore, the system automatically coordinates PV and grid power according to solar availability.
𝐊𝐞𝐲 𝐒𝐲𝐬𝐭𝐞𝐦 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬
Parameter | Value / Operating Condition |
DC Bus Reference Voltage | 400 V |
EV Battery Charging Power | Approximately 1900–2000 W |
Maximum PV Power shown | Approximately 2000 W |
Battery Initial SOC | Approximately 50% |
Battery Operating Mode | Charging |
PV Converter | Boost converter |
Battery Converter | Bidirectional DC–DC converter |
Grid Interface | Full-bridge inverter |
MPPT Technique | Fuzzy Logic MPPT |
Inverter Control | dq-frame current control |
𝐃𝐂 𝐁𝐮𝐬 𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
A stable common DC bus is essential for reliable operation of the charging station.
The DC bus voltage is measured and compared with a reference value of:
400 V
The resulting voltage error is processed by a PI controller.
The controller output is used to generate the PWM command for the battery-side converter.
Main objective
Maintain DC bus voltage near 400 V
Regulate the EV battery charging process
Balance power between PV, battery and grid
Maintain stable converter operation
𝐄𝐕 𝐁𝐚𝐭𝐭𝐞𝐫𝐲 𝐂𝐡𝐚𝐫𝐠𝐢𝐧𝐠 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
The EV battery is connected to the DC bus through a bidirectional DC–DC converter.
In this simulation, the converter is mainly controlled for EV battery charging operation.
The desired battery charging power is approximately:
2 kW
The battery power remains close to this charging reference even when the PV generation changes.
The required charging energy is therefore obtained from:
PV power when solar generation is available
Grid power when PV generation is insufficient
𝐈𝐧𝐯𝐞𝐫𝐭𝐞𝐫 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
The grid-connected inverter regulates the amount of power exchanged with the utility grid.
The control system first evaluates:
Available PV power
Required EV battery charging power
Remaining power that must be exchanged with the grid
For example:
PV Power | EV Charging Requirement | Approximate Grid Requirement |
2000 W | 2000 W | Nearly 0 W |
1000 W | 2000 W | Approximately 1000 W import |
0 W | 2000 W | Approximately 2000 W import |
This demonstrates the main energy-management principle used in the model.
𝐝𝐪-𝐅𝐫𝐚𝐦𝐞 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐂𝐨𝐧𝐭𝐫𝐨𝐥
The inverter uses current regulation based on a dq reference frame.
The grid voltage is used to generate synchronized sine and cosine signals.
The measured inverter current is transformed from the stationary frame into the rotating dq reference frame.
The main control stages are:
Grid voltage measurement
Synchronization
Reference current generation
Current transformation
d-axis current control
q-axis current control
PI current controllers
Inverse transformation
PWM generation
This provides controlled and synchronized grid-current operation.
𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲
The overall control architecture can be summarized as:
PV Measurement → Fuzzy MPPT → Boost Converter Control
DC Bus Voltage → PI Controller → Battery Converter Control
PV Power + Battery Charging Reference → Grid Current Reference
Measured Grid/Inverter Current → dq Current Controller → PWM Inverter
This coordinated strategy enables the charging station to maintain the EV charging requirement under varying PV conditions.
𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬
The simulation provides several useful measurements for analysing the system.
PV measurements
The model displays:
PV voltage
PV current
PV power
As PV operating conditions change, the fuzzy MPPT modifies the converter duty cycle and tracks the available solar power.
Battery measurements
The battery monitoring section provides:
Battery voltage
Battery current
Battery power
State of charge
The simulation shows the battery remaining in charging mode.
The SOC gradually increases from approximately 50%, confirming energy transfer into the EV battery.
Grid measurements
The grid section displays:
Grid voltage
Grid current
Grid power
Grid power automatically changes according to the difference between the available PV power and the required battery charging power.
𝐏𝐨𝐰𝐞𝐫 𝐅𝐥𝐨𝐰 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐮𝐫
The simulation clearly demonstrates the relationship between solar generation and grid support.
Operating Condition | PV Contribution | Grid Contribution | EV Battery |
High PV generation | High | Low or nearly zero | Charging |
Medium PV generation | Partial | Remaining requirement | Charging |
Low PV generation | Low | Increased | Charging |
No PV generation | Zero | Full charging requirement | Charging |
This makes the configuration suitable for studying solar-assisted EV charging under varying renewable-energy availability.
𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬
PV based EV charging station developed in MATLAB/Simulink
Fuzzy Logic MPPT for solar maximum-power extraction
PV interfaced using a boost converter
EV battery connected through a bidirectional DC–DC converter
400 V common DC bus
Grid-connected full-bridge inverter
Controlled EV battery charging near 2 kW
Automatic grid support during low solar generation
dq-frame inverter current control
PI-based DC bus voltage regulation
Monitoring of PV voltage, current and power
Monitoring of battery voltage, current, power and SOC
Grid voltage, current and power measurements
Clear visualization of PV, battery and grid power-sharing behaviour
𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬
This MATLAB/Simulink model can be useful for studying:
Solar-powered EV charging stations
Renewable-energy integration
Grid-supported EV charging
Fuzzy Logic based MPPT techniques
DC–DC converter control
EV battery charging control
DC bus voltage regulation
Grid-connected inverter control
dq-axis current control
PV-grid power management
Smart charging infrastructure
Power-electronics control strategies
𝐖𝐡𝐲 𝐔𝐬𝐞 𝐅𝐮𝐳𝐳𝐲 𝐌𝐏𝐏𝐓?
Fuzzy Logic MPPT provides a flexible method for controlling the PV operating point without relying only on a fixed mathematical operating model.
Important advantages include:
Good response under changing PV conditions
Flexible rule-based control
Suitable for nonlinear PV characteristics
Smooth duty-cycle adjustment
Easy integration with MATLAB/Simulink
Useful for studying intelligent renewable-energy control
𝐖𝐡𝐨 𝐂𝐚𝐧 𝐔𝐬𝐞 𝐓𝐡𝐢𝐬 𝐌𝐨𝐝𝐞𝐥?
The model is suitable for:
Electrical engineering students
Power electronics learners
EV charging researchers
Renewable-energy researchers
MATLAB/Simulink users
Power-system engineers
Control-system engineers
It provides a clear platform for understanding how solar generation, EV charging and grid support can operate together.
𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧
The PV based EV Charging Station with Fuzzy MPPT in MATLAB demonstrates an effective grid-connected charging architecture combining solar PV, an EV battery, DC–DC converters and an inverter.
The fuzzy MPPT controller extracts available solar power, while the EV battery charging controller maintains the desired charging operation. When solar generation decreases, the grid automatically supplies the remaining required power.
The simulation also demonstrates 400 V DC bus regulation, approximately 2 kW EV charging, dq-frame inverter current control, battery SOC variation and coordinated PV-grid power sharing.
Overall, the model provides a practical and easy-to-understand MATLAB/Simulink platform for studying solar-assisted EV charging, fuzzy MPPT, converter control and grid integration.
Comments