Battery Management and Component Optimization

Abstract

With the advent of the electric vehicle era, batteries have become a crucial technology in many industries. They are applied not only in mobility such as electric cars, scooters, and UAMs but also in high-capacity energy storage systems such as ESS, enhancing quality of life. However, batteries are affected by environmental factors like temperature and electric shock, leading to performance degradation. Increased internal resistance and reduced lifespan due to battery aging render continuous operation impossible and can result in sudden accidents. Therefore, research on battery management systems and reducing internal resistance in the initial battery manufacturing stages is crucial. This thesis addresses these two problems one in each chapter. In Chapter 1, the impact of heat generation in drone systems on batteries is examined, and an improved battery estimation algorithm is developed for the BMS system of drone batteries. To achieve this, a framework consisting of offline and online states is proposed. In the offline state, battery capacity estimation is performed using current pulses while, in the online state, an adaptive algorithm is used for State-of-Charge estimation. Validation is conducted on two batteries with different cycles, demonstrating more reliable SoC estimation results compared to the existing BMS. In Chapter 2, research is conducted to optimize the ionic conductivity of battery electrolytes in the initial battery synthesis stage. Bayesian optimization is employed as the iterative design methodology and a total of six design iterations are performed. The experimental results show that optimized mole fraction yields an increased conductivity by 9% compared to initial maximum value and thus reducing the battery resistance in synthesis stage.