Paper Title
Analyzing The Effectiveness of Traditional and Adaptive Kalman Filters for Precise Soc Calculation

Abstract
The contrasts between the Adaptive Kalman Filter (AKF) and the classical Kalman Filter (KF) are examined in this work. One popular recursive approach for estimating the state of linear systems with Gaussian noise is the Kalman Filter. It does, however, make the assumption that the measurement and process noise covariances are known and constant, which isn’t always feasible in practical situations. In order to increase estimation accuracy in uncertain or timevarying situations, the Adaptive Kalman Filter, an extension of the Kalman Filter, dynamically modifies the noise covariances. In order to shed light on the benefits and drawbacks of each strategy, this study examines the mathematical underpinnings, uses, and restrictions of each. Keywords - State estimation, process noise, measurement noise, noise covariance, dynamic environments, time-varying noise, Gaussian noise, linear systems, recursive filtering, Kalman Filter (KF), Adaptive Kalman Filter (AKF), and estimate accuracy.