OPTIMIZATION OF BATTERY

This thesis presents the optimization of a battery management system (BMS) for nano satellites, aimed at enhancing their operational efficiency and longevity. The objective of the research was to develop a systematic methodology for optimizing the charging and discharging processes of lithium-ion batteries used in these systems. Utilizing a combination of algorithms including Social Group Optimization (SGO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), the study conducted simulations to analyze the performance of the BMS under various operational conditions. The methodology involved the modeling of battery parameters and the implementation of optimization algorithms to identify the most favorable charging strategies. Results from the simulations indicated significant improvements in battery performance metrics, such as state of charge management and overall energy efficiency. The findings demonstrate that the optimized BMS can effectively enhance the operational efficiency of nano satellites, leading to more reliable missions in space. This research provides a foundational approach for future developments in satellite technology, particularly in optimizing energy resources for extended missions.