An exploratory study on intelligent active cell balancing of electric vehicle battery management and performance using machine learning algorithms

• Presented an accurate solution for optimizing BMS through machine learning-based active cell balancing. • PA-RNN, Deep-Q, AQN, ADNN & AC enhance SoC accuracy and control. • Automotive Controllers are EV-specific for improved performance. Battery Management Systems (BMS) rely on cell balancing to extend the longevity and efficiency of battery packs. Among these, active cell balancing techniques offer significant advantages over passive methods by redistributing charge more effectively, resulting in improved voltage balance, increased battery capacity, and an extended lifecycle. However, active cell balancing is often more complex, costly, and may introduce heat management challenges. Recently, machine learning (ML)-based active cell balancing controllers have emerged as a promising solution to optimize these systems. Leveraging dynamic charge control with probability distributions and advanced instrumentation, ML algorithms such as Predictive Analytic Recurrent Neural Networks (PA-RNN), Deep-Q Networks (DQN), Amortized-Q Networks (AQN), Adaptive Neural Networks (ADNN), and Automotive Controllers (AC) have demonstrated precise control over battery voltage, current, and State of Charge (SoC). These approaches significantly reduce SoC discrepancies, enhance energy efficiency, and mitigate common battery issues. Comparative analyses reveal that ML -driven active cell balancing not only outperforms traditional methods but also offers substantial improvements in performance, safety, and battery lifespan. This exploratory study highlights the disruptive potential of machine learning algorithms in revolutionizing Battery Management Systems, advancing both operational efficiency and overall battery health.