Active cell balancing in battery management using AI algorithm

With the rising adoption of electric vehicles (EVs) and renewable energy technologies, managing battery systems efficiently has become essential to ensure enhanced performance, reliability, and longevity of battery energy storage systems (BESS). One key technique in this context is active cell balancing, which ensures that all cells within a battery pack maintain consistent charge levels, thereby avoiding issues such as overcharging or deep discharging individual cells. However, conventional balancing approaches typically fall short when it comes to adaptability and responsiveness under varying operational conditions. To overcome these challenges, this research explores the integration of artificial intelligence (AI) methods into active cell balancing frameworks. By utilizing AI techniques including reinforcement learning, neural networks, and fuzzy logic, the system can learn and anticipate cell behavior, dynamically regulate balancing currents, and respond effectively to real-time changes. This intelligent balancing method enhances the uniformity of the state of charge (SoC), improves thermal management, and ultimately increases the overall efficiency and lifespan of the battery pack. Simulated outcomes and comparative assessments validate the superiority of the AI-based approach over traditional methods, pointing to its potential in shaping future intelligent battery management systems.