Altered Artificial Algae Algorithm (AAAA) based Optimized Battery Cell Balancing using T-shaped H-Bridge (THB) Multi Level Converter

Abstract

Battery cell imbalance remains a major challenge in series-connected lithium-ion battery packs, affecting efficiency, safety, and lifespan. Variations in manufacturing, temperature distribution, and aging lead to unequal states of charge (SOC) among cells, resulting in reduced usable capacity and increased risk of degradation. To address these issues, an optimized battery balancing approach based on an Altered Artificial Algae Algorithm (AAAA) integrated with a T-shaped H-bridge (THB) multilevel converter was presented. The proposed method formulates the balancing problem as an optimization task aimed at minimizing SOC deviation across cells. The AAAA enhances the conventional Artificial Algae Algorithm by incorporating adaptive energy updating, improved exploration–exploitation balance, and mutation-based diversity mechanisms, enabling faster convergence and avoidance of local minima. The THB multilevel converter facilitates efficient energy redistribution with reduced switching losses and improved voltage control. A simulation model was developed in MATLAB/Simulink to evaluate system performance under varying imbalance conditions. Results demonstrate significant improvements in balancing speed, energy efficiency, and SOC uniformity compared to passive methods and conventional optimization techniques such as GA and standard AAA. The proposed approach achieves faster convergence, reduced power loss, and stable operation under dynamic conditions. The integration of advanced optimization with an efficient converter topology provides a robust solution for modern battery management systems, particularly in electric vehicles and renewable energy applications.