Modeling and Parameters Estimation of Battery Bank by Using Modified Grey Wolf Optimization

This study addresses the challenge of modeling and parameter estimation in battery banks for efficient energy storage systems. With the increasing complexity of battery packs, consisting of thousands of cells with uneven degradation, accurate modeling is critical. The research aims to develop a precise battery model and state of charge (SOC) estimator to optimize performance and prevent overcharging or over-discharging. A MATLAB/Simulink-based model was constructed, incorporating various factors such as load profiles and operational conditions. To enhance parameter estimation accuracy, a novel Modified Grey Wolf Optimization (MGWO) method was proposed. The methodology demonstrated reliable parameter estimation and system response under diverse conditions. Results indicate that MGWO significantly improves model accuracy and operational reliability. This advancement contributes to optimizing battery system design, extending lifespan, and enhancing energy efficiency. The findings have broad implications for sustainable energy storage and integration in modern applications.