Accurate assessment of lithium-ion battery State of Health (SOH) is critical for the reliability and safety of electric vehicles. However, conventional Electrochemical Impedance Spectroscopy (EIS) parameter extraction methods suffer from high computational complexity and sensitivity to initial values. This paper proposes a geometric analysis-based approach for rapid ohmic resistance extraction to efficiently evaluate battery health status. To address the limited frequency range (1–100 Hz) caused by onboard hardware constraints, the method employs nonlinear least squares fitting of impedance data in the 25–100 Hz band to precisely extract ohmic resistance parameters, while validating its effectiveness as an SOH indicator. Experimental results demonstrate strong correlation between ohmic resistance and SOH (Pearson coefficient >0.92) under moderate temperature conditions, with negligible influence from State of Charge (SOC). With a goodness-of-fit exceeding 0.98, this method provides a high-precision, low-computational solution for real-time battery health monitoring in battery management systems. The proposed approach achieves reliable SOH estimation while significantly reducing computational overhead compared to traditional methods.