The global challenges of energy shortages and environmental pollution have driven the rapid adoption of electric vehicles (EVs) as clean energy solutions. Lithium-ion batteries, essential for EVs, provide high energy density and long lifespan but are characterized by complex nonlinear dynamics, which complicate their State of Charge (SOC) estimation. This paper presents an advanced SOC estimation method for lithium-ion batteries using a fractional-order second-order RC model. The model integrates fractional calculus to better capture the complex electrochemical processes in batteries, thereby addressing the limitations of traditional integer-order models. To identify the model parameters, a Particle Swarm Optimization (PSO) algorithm was employed, followed by implementation of the Fractional-order Square Root Cubature Kalman Filter (FSRCKF) to estimate the SOC. Experimental validation using Dynamic Stress Test (DST) conditions demonstrates that the FSRCKF-based SOC estimation offers higher accuracy and robustness than traditional methods. The proposed approach enhances the precision of battery management systems for EVs and energy-storage applications.