Which parameter estimation technique is most suitable for real-time adaptation to changing battery dynamics, and why?

Recursive Least Squares (RLS) with an adaptive forgetting factor is generally the most suitable parameter estimation technique for real-time adaptation to changing battery dynamics. Batteries exhibit dynamic behavior due to factors like temperature variations, aging, and changes in operating conditions (charge/discharge rates). RLS is an online, iterative algorithm that continuously updates parameter estimates based on new measurements. It is computationally efficient and well-suited for real-time implementation within a Battery Management System (BMS). The 'least squares' part refers to minimizing the sum of the squares of the errors between the model's predictions and the actual battery behavior. The 'recursive' aspe....