Battery states co-estimation technique using dual square root unscented kalman filter

A method of estimating battery states, the method comprising:

• creating an enhanced 2-RC circuit battery model as the electrical equivalent circuit of a battery, wherein the model accounts for nonlinear polarization effects and dynamic parameter adaptation;
• performing an incremental open circuit voltage (OCV) test to extract a plurality of 2-RC model parameters, wherein the parameters include internal resistance, temperature, OCV, and time-dependent voltage relaxation characteristics;
• estimating a State of Charge (SOC) of the battery using a modified Dual Square Root Unscented Kalman Filter (D-SRUKF) based on a multi-factor co-estimation technique that simultaneously refines model parameters using a covariance-matching algorithm;
• estimating a State of Health (SOH) of the battery based on a real-time adaptive degradation model, incorporating SOC, OCV, and historical cycle degradation trends of the 2-RC circuit battery model;
• estimating a State of Power (SOP) of the battery based on dynamic real-time power constraints, maximum deliverable power, maximum allowable voltage, the internal resistance, and the OCV, wherein the SOP estimation compensates for transient voltage recovery effects and load-dependent resistance variations.