SOC Estimation Using Extended Kalman Filter (EKF) for Batteries

The Extended Kalman Filter (EKF) serves as a powerful algorithm for estimating battery State of Charge (SOC), which represents the remaining capacity of a battery. This algorithm extends the standard Kalman filter framework to handle nonlinear system dynamics commonly found in battery models. The implementation typically involves using real-time battery current and voltage measurements to recursively predict and update the SOC value through a two-step process: prediction (time update) and correction (measurement update). In practical code implementation, key functions would include: - System modeling using battery equivalent circuit models (e.g., Thevenin model) - Linearization of nonlinear battery equations through Jacobian matrix calculations - Covariance matrix updates for uncertainty quantification - Adaptive tuning of process and measurement noise parameters The algorithm's significance lies in its ability to provide accurate SOC estimates, enabling better battery management and preventing unexpected device shutdowns. Beyond battery applications, EKF finds extensive use in other technical domains including navigation systems, robotics, and automotive control systems. Therefore, research and application of EKF algorithms hold substantial importance and value across multiple engineering fields.