Enhancing Q‐Learning via State‐Space Design for Active Battery Balancing

ABSTRACT Active battery balancing is essential for maximizing the performance and safety of lithium‐ion battery packs in electric vehicles and energy storage systems, yet traditional control methods struggle with nonlinear dynamics. This paper investigates the critical role of state‐space design in tabular Q‐learning for controlling switches of a buck‐boost converter in a four‐cell pack, addressing a key gap in the application of reinforcement learning to battery management systems. We propose and compare three novel discrete state representations: a coarse 11‐state pairwise comparison, an intermediate 27‐state hierarchical relational model, and a fine‐grained 81‐state individual deviation model. Through simulations across 1000 training episodes and 24 test scenarios, the 27‐state model achieves superior convergence, with an average balancing time of around 41 timesteps and the lowest performance variance (σ = 12.28). Statistical analysis and state‐transition graphs reveal that this optimal granularity enables hierarchical control strategies, balancing informational richness with learnability to avoid perceptual aliasing and the curse of dimensionality. These findings provide a blueprint for designing efficient RL policies in BMS, which has implications for scalable and real‐time implementations in high‐voltage applications.