SOC Estimation Using Kalman Filter

This program employs a Kalman Filter to estimate battery State of Charge (SOC). The Kalman Filter algorithm serves as an optimal estimator for unknown system states by dynamically integrating system models and measurement data to minimize estimation errors. In our implementation, we model the battery as a dynamic system where current and voltage measurements serve as observational inputs. The core algorithm involves two main phases: prediction (using system dynamics) and update (incorporating measurement corrections). Key implementation aspects include defining state transition matrices, measurement models, and noise covariance matrices specific to battery characteristics. Through this implementation, users can gain insights into how Kalman Filters handle common SOC estimation challenges such as measurement noise, model inaccuracies, and initial state uncertainty. The code demonstrates practical techniques for real-time SOC tracking while maintaining computational efficiency. We hope this program serves as a valuable resource for understanding Kalman Filter applications in battery management systems.