🔋 State of Charge (SOC) Estimation of Li-Ion Battery Extended Kalman Filter (EKF) | MATLAB Simulink

Описание: 🔋 State of Charge (SOC) Estimation of Li-Ion Battery using Extended Kalman Filter (EKF) | MATLAB Simulink

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This video presents State of Charge (SOC) estimation of a Lithium-Ion battery using the Extended Kalman Filter (EKF) in MATLAB Simulink. The model combines an equivalent circuit battery model (ECM) with an EKF observer to estimate SOC accurately under dynamic load and drive-cycle conditions.

You’ll see how EKF uses measured terminal voltage and current to correct SOC in real time, improving estimation compared to simple Coulomb counting—especially under noise, parameter variation, and changing operating conditions.

✅ Best for EV battery management systems (BMS), energy storage, and control-oriented battery modeling projects.

🔍 Key Topics Covered

Li-ion battery modeling (ECM: R0–R1C1 / RC network)
OCV–SOC curve and parameter identification
EKF prediction + correction steps
SOC estimation under pulse load / drive cycle
Error analysis and convergence performance
MATLAB Simulink implementation (BMS-ready structure)

🎯 Applications

EV Battery Management System (BMS)
Hybrid EV / Microgrid energy storage
Real-time SOC monitoring & estimation
Research projects (MSc/PhD) in EV & control

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