<p>The growing demand for high-performance energy storage systems, particularly in electric vehicles and renewable energy applications, has amplified the need for efficient battery management systems. Central to a BMS is the ability to ensure cell voltage uniformity through active cell balancing, which enhances energy utilization, safety, and battery longevity. Traditional active balancing techniques, such as switched capacitor and transformer-based methods, face challenges including limited scalability, slower balancing speeds, and increased computational complexity. To address these limitations, this paper proposes a novel hybrid active balancing approach that integrates switched capacitor and transformer-based techniques, dynamically controlled by a quantum particle swarm optimization algorithm. The hybrid system combines the speed of switched capacitor balancing for localized voltage differences with the long-range capabilities of transformer-based balancing, enabling efficient energy redistribution across large battery packs. The QPSO algorithm optimizes balancing strategies in real time, reducing computational load while achieving faster convergence and energy efficiency. Simulations and experimental conditions are used to test the proposed system, and they turn out to be more efficient compared to the existing optimization techniques, which include genetic algorithm, salp swarm algorithm, and gorilla troops optimization algorithm. The findings show a tremendous increase in the balancing efficiency (99.24 percent), faster convergence of voltages, and lesser energy losses. A computationally feasible, efficient, and scalable solution to next-generation BMS presented in this work would be very suitable in the application of EVs and renewable energy storage systems. </p>