Deep Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

The growing adoption of Electric Vehicles (EVs) has led to the need for more efficient, reliable, and long-lasting battery systems. One of the critical issues in EV battery management is the imbalance between individual cells in the battery pack, which can affect the overall performance, safety, and lifespan of the battery. This project proposes a deep learning-based optimal cell balancing mechanism for the Battery Management System (BMS) of EVs. By leveraging deep learning algorithms, this research aims to develop a mechanism that can predict and correct cell imbalances in real-time, ensuring optimal performance and longevity of the battery. The model will use data from various sensors to monitor the state-of-health (SoH) and state-of-charge (SoC) of each cell, enabling dynamic adjustments for efficient energy distribution across the battery pack. The approach aims to outperform traditional model-based or heuristic methods in terms of accuracy, adaptability, and scalability.