Battery packs composed of mixed cell chemistries or aged cells present unique challenges for cell balancing. Traditional passive or active balancing methods, designed for homogeneous packs, often fail to account for the varying characteristics of dissimilar cells. Adaptive balancing strategies, particularly those leveraging machine learning, offer a promising solution to optimize performance and extend the lifespan of heterogeneous battery systems.

In hybrid systems such as Li-ion/NiMH packs or second-life battery applications, cells exhibit different voltage curves, capacities, and degradation rates. Static balancing thresholds, typically set for uniform cells, become inefficient. Instead, dynamic balancing adjusts thresholds based on real-time data, accommodating differences in state of charge (SOC), internal resistance, and aging effects. Machine learning enhances this process by predicting optimal balancing actions through pattern recognition and adaptive algorithms.

One approach involves reinforcement learning (RL), where an algorithm learns the best balancing strategy through trial and error. The RL agent observes parameters such as cell voltages, temperatures, and historical performance, then adjusts balancing currents to minimize divergence between cells. Over time, the system identifies the most effective actions for specific operating conditions, improving efficiency compared to fixed-rule methods.

Another method employs supervised learning to predict cell behavior under different loads. By training models on datasets from hybrid or aged packs, the system can forecast voltage drift and capacity fade. These predictions inform balancing decisions, such as prioritizing cells with higher degradation rates or adjusting charge redistribution to prevent overvoltage in weaker cells. Neural networks, particularly long short-term memory (LSTM) models, excel at capturing time-dependent patterns in battery data, making them suitable for this application.

Hybrid Li-ion/NiMH systems benefit from adaptive balancing due to their differing charge/discharge characteristics. NiMH cells tolerate overcharge better than Li-ion but have lower energy density and higher self-discharge. A machine learning-based balancer can allocate charge currents proportionally, ensuring Li-ion cells remain within safe voltage limits while maximizing NiMH utilization. For example, during regenerative braking in electric vehicles, the balancer may divert excess energy to NiMH cells if Li-ion cells approach their upper SOC threshold.

Second-life battery applications, where retired EV cells are repurposed for grid storage, face significant variability in cell health. Adaptive balancing mitigates this by grouping cells with similar degradation profiles and applying customized thresholds. Clustering algorithms, such as k-means or hierarchical clustering, classify cells based on capacity, impedance, and cycle history. The balancer then tailors its strategy for each cluster, reducing stress on weaker cells and improving overall pack efficiency.

A key advantage of machine learning in this context is its ability to handle nonlinear relationships. Battery aging involves complex interactions between factors like temperature, cycling depth, and charge rates. Traditional models struggle to capture these dynamics, whereas machine learning algorithms adapt as new data becomes available. For instance, a support vector machine (SVM) can classify cells into high-risk or low-risk categories based on early signs of degradation, enabling preemptive balancing adjustments.

Implementation challenges include computational overhead and the need for extensive training data. Edge computing solutions address the former by deploying lightweight models on embedded BMS hardware. Federated learning, where multiple packs share insights without exposing raw data, helps overcome data scarcity while preserving privacy.

In summary, adaptive balancing strategies powered by machine learning are critical for optimizing performance in packs with mixed chemistries or aged cells. By dynamically adjusting thresholds and leveraging predictive analytics, these systems enhance safety, efficiency, and longevity. Applications in hybrid Li-ion/NiMH systems and second-life batteries demonstrate the versatility of these approaches, paving the way for more sustainable and reliable energy storage solutions.

Future advancements may integrate digital twin technology, enabling real-time simulation of balancing outcomes before physical adjustments. Combined with evolving algorithms, this could further refine the precision of adaptive balancing for heterogeneous battery packs.