LMS Solution

⚡ Online MATLAB Workshop: Designing of PV–Wind Powered EV Charging Stations
Join our practical online workshop and learn how to design and simulate a PV–Wind Powered EV Charging Station using MATLAB/Simulink.

Registration link : https://zurl.co/rYZ1w

📅 Date: 20th June 2026
🌐 Mode: Online via Google Meet
💰 Fee: From ₹499
💻 Software: MATLAB/Simulink

What You Will Learn

✅ PV system modeling
✅ Wind energy system modeling
✅ MPPT control implementation
✅ Battery storage integration
✅ DC charging system design
✅ Grid inverter modeling
✅ EV battery charging simulation
✅ AC grid integration
✅ Energy management and power flow analysis

Participants Will Receive

🎓 Workshop Certificate
🎥 Video Recording
🧩 MATLAB/Simulink Simulation Model
💻 MATLAB Code Files

This workshop is suitable for students, research scholars, faculty members, and engineers working in renewable energy, EV charging systems, and MATLAB/Simulink-based modeling.

🚀 Limited Seats Available
📌 Book Now and upgrade your MATLAB renewable energy simulation skills!

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⚡ Online MATLAB Workshop: Designing of PV–Wind Powered EV Charging Stations

Registration Link: https://zurl.co/kkqps

📅 Workshop Date: 20th June 2026
🌐 Mode: Online via Google Meet
💰 Fee: From ₹499
💻 Software: MATLAB/Simulink

Join our online MATLAB workshop and learn how PV, wind, battery storage, grid inverter, and EV charging systems can be modeled using MATLAB/Simulink. This workshop covers renewable EV charging station design, MPPT control, DC charging operation, grid integration, and power management.

🔹 Session 1: Modeling of Grid-Connected PV–Wind Systems

☀️ PV System Modeling – Learn the behavior of solar PV under changing irradiance and temperature.
🌬️ Wind System Modeling – Simulate wind power generation under different wind speed conditions.
📈 MPPT Control – Implement MPPT algorithms for maximum power extraction.
🔋 Battery Storage – Model battery energy storage for renewable power support.
⚡ DC Charging System – Design DC charging operation using PV and wind power.
🔁 Power Flow Management – Analyze power sharing between PV, wind, battery, and DC load.

🔹 Session 2: Modeling of PV–Wind Based EV Charging Station

🚗 EV Charging Station Modeling – Develop a complete PV–wind powered EV charging system.
🔌 EV Battery Charging – Simulate EV battery charging using renewable energy.
🔄 Grid Inverter Modeling – Model PV–wind grid inverter operation in MATLAB/Simulink.
🏭 AC Grid Integration – Integrate PV, wind, battery, EV battery, and AC grid support.
🧠 Energy Management – Manage power between renewable sources, storage, EV load, and grid.

🎯 Key Learning Outcomes:
✅ Model PV and wind systems
✅ Implement MPPT algorithms
✅ Design battery-supported DC charging systems
✅ Simulate PV–wind EV charging station operation
✅ Analyze power flow between PV, wind, battery, EV, and grid

🚗⚡ ANFIS MPPT and Neural Network Based Energy Management for Solar PV Powered EV Charging Station

Renewable energy-based EV charging stations are becoming an important solution for reducing grid dependency and improving sustainable transportation. In this work, a Solar PV–Battery–Grid integrated EV charging station is developed in MATLAB/Simulink using intelligent control techniques.

Purchase link : https://zurl.co/iSjVC

The proposed system uses ANFIS-based MPPT control to extract maximum power from the PV array under varying irradiance and temperature conditions. The ANFIS controller provides accurate reference generation and improves PV power tracking performance.

A Neural Network based Energy Management System is implemented to manage power sharing among the PV system, battery storage, EV battery, and utility grid. The trained neural network predicts the required reference current with high accuracy, enabling smooth charging control and stable power balance.

Simulation results show:
✅ Effective maximum power extraction from solar PV
✅ Stable DC-link voltage regulation
✅ Proper EV battery charging operation
✅ Controlled battery storage performance
✅ Grid import/export based on power demand
✅ High ANN prediction accuracy with regression close to 1

This intelligent control approach is suitable for future smart EV charging stations, renewable energy integration, and grid-connected battery-supported charging infrastructure.