<p>The growing mismatch between energy generation and consumption highlights the need for efficient energy storage solutions. Lithium-ion batteries dominate current applications due to their high energy density and long cycle life, yet their safe and optimal operation relies heavily on accurately monitoring two internal states: the state of charge (SOC) and the core temperature. Real-time estimation of these states remains difficult because the battery’s electrical and thermal behaviors are strongly coupled and vary with operating conditions. This paper presents an enhanced electro-thermal modeling and estimation framework that explicitly accounts for the mutual dependence between SOC and core temperature. Unlike conventional fixed-parameter approaches, the model parameters are defined as nonlinear functions of SOC and core temperature, capturing their inherent interdependence. These relationships are identified experimentally through controlled charge–discharge tests at 5°C, 25°C, and 45°C. A physics-based thermal model is used offline as a virtual core temperature sensor to generate reference temperature trajectories for parameter identification, since only surface and ambient temperatures are measured. The ARMAX technique is applied to estimate the electrical and thermal sub-model parameters from experimental data, and an Extended Kalman Filter is subsequently implemented on the coupled nonlinear model to jointly estimate SOC and core temperature under varying load and ambient conditions. Simulation results demonstrate that the proposed method offers higher accuracy and robustness than traditional fixed-parameter models, reducing mean estimation errors by more than 40% under both nominal and noisy scenarios. The improved precision and resilience make the approach well suited for integration into advanced Battery Management Systems.</p>