Thermal and electrical behavior analysis of LFP-cathode battery

Traditional state-of-charge (SOC) estimation frameworks for lithium-iron-phosphate (LiFePO $$_{4}$$ ) batteries remain heavily reliant on extensive, empirical test data to parameterize equivalent circuit models. This reliance creates significant development bottlenecks and limits predictive capability under dynamically shifting thermal environments. This paper introduces a novel, multi-scale, physics-informed simulation pipeline that bridges sub-atomic quantum metrics directly with macroscopic real-time state estimation. Instead of relying on purely empirical tracking configurations, we extract nominal open-circuit voltage (OCV) input vectors from first-principles Density Functional Theory atomistic models that define the crystal structure and charge-transfer pathways of the LFP cathode material. These quantum-mechanical parameters are scaled and mapped directly onto a temperature-dependent second-order RC Thevenin circuit layout deployed within the MATLAB Simulink/Simscape environment and employed the modified Extended Kalman Filter (mEKF) algorithm into the simulation block nodes. The model is validated against benchmark experimental data under constant-current discharge, CC-CV charging, pulse-discharge, and Dynamic Stress Test (DST) operating conditions, and demonstrated an improved estimation. The resulting framework was evaluated across a wide ambient temperature envelope and charging/discharging rates.