Reinforcement Learning Techniques for Real Time Battery Cell Balancing Using Modified Cascaded H-Bridge Converters

This study presents an innovative approach to individual battery cell balancing, focusing on the implementation of a modified cascaded H-Bridge (MCHB) multilevel converter utilizing a reinforcement learning (RL) protocol.In battery systems, particularly those employing extended-lifecycle cells that exhibit intrinsic mismatches, achieving effective cell balancing is crucial for enhancing overall performance and lifespan.The proposed design connects each converter module directly to individual battery cells, facilitating precise balancing without the need for additional complex circuitry.This approach is especially beneficial for applications involving elongated facilitylifecycle grid storage, where optimal performance and reliability are paramount.The research employs an advanced control technique driven by reinforcement learning algorithms to dynamically regulate the interaction between the Battery State of Charge (SoC) and the outputs of the modified cascaded H-Bridge.By continuously adapting to varying operational conditions, the RL protocol ensures that each cell is charged and discharged optimally, minimizing the effects of imbalance and maximizing the overall efficiency of the battery system.The implementation of this technique allows for real-time monitoring and adjustment, significantly improving the response time and accuracy of the balancing process.In the context of grid storage applications, this novel methodology not only enhances the longevity of battery cells but also contributes to more sustainable energy management practices.The experimental results demonstrate that the RL-based MCHB converter significantly outperforms traditional balancing methods, offering a promising solution for future battery management systems.This work paves the way for the development of more efficient energy storage solutions, ultimately supporting the transition to cleaner energy sources and advancing the field of energy technology.Further research will focus on optimizing the RL algorithms and exploring their applicability in various energy storage scenarios.