Kalman Filter: Battery State of Charge (SOC) Estimation Model

In electric and hybrid electric vehicles, monitoring battery remaining capacity is crucial. The Kalman Filter serves as an effective model for estimating battery state and State of Charge (SOC). This recursive algorithm utilizes previous state estimates and current measurement data to predict present battery conditions and SOC values. The implementation typically involves two main phases: prediction (using system dynamics equations) and correction (incorporating measurement updates). In practical applications, Kalman Filters are extensively employed for tracking object position and velocity, as well as estimating various physical quantities and parameters. The core algorithm operates through state transition matrices (F), control-input models (B), and observation matrices (H), with covariance matrices (Q and R) handling process and measurement uncertainties. This powerful technique finds applications across multiple domains including navigation systems, control engineering, signal processing, telecommunications, and robotics. Code implementation often involves initializing state variables, defining system matrices, and iterating through prediction-correction cycles using equations like x = Fx + Bu + w (prediction) and y = z - Hx (measurement residual).