ANFIS Based MPPT and EMS for PV Grid Powered EV Charging Station in MATLAB
ANFIS Based MPPT and Energy Management for PV Grid Powered EV Charging Station in MATLAB Simulink
𝐅𝐮𝐥𝐥 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧:
This MATLAB Simulink model presents an advanced ANFIS based MPPT and Energy Management System for a PV grid powered EV charging station. The system is designed to extract maximum power from the solar PV array, regulate the DC bus voltage, control EV battery charging, manage battery energy storage, and coordinate power exchange with the utility grid.
The model integrates solar PV generation, ANFIS based maximum power point tracking, battery energy storage system, EV battery charging unit, bidirectional converter control, DC link voltage regulation, grid-connected inverter, and grid real/reactive power analysis. This simulation is highly suitable for renewable energy based EV charging station analysis, intelligent control studies, power electronics research, and MATLAB Simulink based academic learning.
𝐒𝐲𝐬𝐭𝐞𝐦 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰:
The proposed system uses solar PV as the main renewable energy source. The PV array is connected to the DC bus through a controlled power converter. An ANFIS based MPPT controller is used to generate the optimum PV voltage reference under variable irradiance and temperature conditions. This helps the PV array operate close to its maximum power point.
A battery energy storage system is connected to the DC bus to support the charging station during low PV generation conditions. The EV battery charging system receives controlled power from the DC link. The utility grid is connected through a grid-side inverter, which supports the system whenever PV and battery power are not sufficient.
𝐌𝐚𝐢𝐧 𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬:
Software Platform: MATLAB Simulink
Control Method: ANFIS based MPPT and ANFIS based EMS
Main Source: Solar PV array
Additional Source: Utility grid
Storage System: Battery Energy Storage System
Load Type: EV battery charging system
DC Link: Regulated DC bus system
Converter System: DC-DC converter and grid-connected inverter
MPPT Input Parameters: Irradiance and temperature
MPPT Output Parameter: PV voltage reference
EMS Input Parameters: PV power and battery SOC
EMS Output Parameter: Current reference for energy management
Simulation Outputs: PV voltage, PV current, PV power, irradiance, BESS voltage, BESS current, BESS power, BESS SOC, EV battery voltage, EV charging current, EV battery power, EV SOC, DC bus voltage, grid voltage, grid current, inverter current, grid active power, and grid reactive power
𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧:
The operation starts from the solar PV array. The PV array receives irradiance input, and its voltage and current are measured. Based on the PV operating condition, the ANFIS based MPPT controller predicts the optimum voltage reference. The converter control uses this reference to extract the maximum available power from the PV source.
The generated PV power is supplied to the common DC bus. The DC bus acts as the central power exchange point for PV, battery, EV charging system, and grid interface. The DC bus voltage is regulated to maintain stable operation of the charging station.
The battery energy storage system supports the DC bus during low PV generation. When PV power is reduced due to low irradiance, the battery and grid provide additional power to continue EV charging. When excess PV power is available, the battery can absorb the surplus power depending on the energy management condition.
The EV battery is charged through a controlled DC-DC converter. The charging current and power are controlled according to the EMS decision. The EV battery result shows charging operation through negative battery current and negative power, while the SOC gradually increases during the simulation.
The grid-connected inverter controls the power exchange between the DC bus and the AC grid. Grid voltage, grid current, inverter current, active power, and reactive power are monitored to evaluate grid-side performance. When PV power decreases, the grid real power increases to support the EV charging demand.
𝐀𝐍𝐅𝐈𝐒 𝐌𝐏𝐏𝐓 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬:
The ANFIS based MPPT controller is trained using irradiance, temperature, and PV voltage data. The trained controller predicts the suitable PV voltage reference for different environmental conditions. The MPPT training and testing results show accurate prediction, low error, and strong agreement between actual and predicted PV voltage. This confirms that the ANFIS MPPT controller can track the maximum power point effectively under changing solar conditions.
𝐀𝐍𝐅𝐈𝐒 𝐄𝐌𝐒 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬:
The ANFIS based Energy Management System is developed to manage power flow between PV, BESS, EV battery, and grid. Battery SOC and PV power are used as important input parameters. The EMS predicts the required current reference for controlling charging, discharging, and grid power support. The EMS training and testing results show good prediction performance and reliable current reference generation.
𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐮𝐥𝐭𝐬:
The PV result shows that PV current and PV power decrease when irradiance decreases step by step. The PV voltage remains nearly regulated due to MPPT control. The BESS result shows battery voltage, battery current, battery power, and SOC variation. The EV battery result confirms charging operation, where the EV SOC increases during simulation. The DC bus and grid result shows stable DC bus voltage regulation, sinusoidal grid voltage, controlled grid current, and inverter current response. The grid real and reactive power result confirms that the grid supports the system during reduced PV generation.
𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬:
Solar PV based EV charging station simulation
ANFIS based MPPT controller analysis
Intelligent energy management system design
Battery energy storage system control study
DC bus voltage regulation analysis
Grid-connected inverter control study
Renewable energy and smart grid simulation
EV battery charging control analysis
Power flow management between PV, battery, EV, and grid
Research and academic learning in MATLAB Simulink
𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬:
Intelligent MPPT control using ANFIS
Efficient PV power extraction under variable irradiance
Coordinated energy management between PV, battery, EV, and grid
Stable DC bus voltage regulation
Controlled EV battery charging operation
Grid real and reactive power performance analysis
Complete training, testing, and simulation result visualization
Suitable for renewable energy, EV charging, and smart grid studies
