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' aspect means it updates its estimates with each new data point, rather than requiring all data to be processed at once. The key to its real-time adaptability lies in the 'adaptive forgetting factor'. A forgetting factor (typically a value between 0 and 1) discounts the influence of older data points on the current parameter estimates. This is crucial because battery parameters change over time, and relying too heavily on past data can lead to inaccurate estimates. When battery dynamics change rapidly (e.g., during a high-current pulse or a sudden temperature shift), the forgetting factor allows the algorithm to quickly 'forget' the old parameter values and adapt to the new dynamics. An adaptive forgetting factor further enhances this capability by automatically adjusting the forgetting rate based on the observed changes in the battery's behavior. For example, if the algorithm detects a sudden change in the battery's voltage response, it will decrease the forgetting factor to give more weight to recent data, allowing it to quickly adapt to the new dynamics. Conversely, if the battery behavior is stable, the forgetting factor will increase to reduce the impact of noise and improve the accuracy of the parameter estimates. While other techniques like Kalman Filtering can also be used for online parameter estimation, they are often more computationally intensive and require accurate knowledge of process and measurement noise characteristics, which can be difficult to obtain in real-time. RLS with an adaptive forgetting factor provides a good balance between accuracy, computational efficiency, and adaptability, making it a preferred choice for real-time battery parameter estimation.