How do adaptive forgetting factors in RLS algorithms specifically improve parameter tracking in batteries experiencing capacity fade?

Adaptive forgetting factors in Recursive Least Squares (RLS) algorithms significantly improve parameter tracking in batteries experiencing capacity fade by allowing the algorithm to prioritize recent data over older data, enabling it to quickly adapt to changes in battery parameters that occur as the battery degrades. RLS is an online parameter estimation technique that recursively updates the estimates of a model's parameters based on new measurements. It's used to estimate the parameters of a battery model (e.g., an equivalent circuit model) in real-time. Capacity fade, a key indicator of battery aging, refers to the gradual reduction in the maximum amount of charge that a battery can store. As a battery experiences capacity fade, its parameters, such as internal resistance and open-circuit v....