The lithium iron phosphate (LFP) cell chemistry is finding wide acceptance for energy storage on-board hybrid electric vehicles (HEVs) and electric vehicles (EVs), due to its high intrinsic safety, fast charging, and long cycle life. However, three main challenges need to be addressed for the accurate estimation of their state of charge (SOC) at runtime: Long voltage relaxation time to reach its open circuit voltage (OCV) after a current pulseTime-, temperature- and SOC-dependent hysteresisVery flat OCV-SOC curve for most of the SOC rangeIn view of these problems, traditional SOC estimation techniques such as coulomb counting with error correction using the SOC-OCV correlation curve are not suitable for this chemistry.This work addressed these challenges with a novel combination of the extended Kalman filter (EKF) algorithm, a two-RC-block equivalent circuit and the traditional coulomb counting method. The simplified implementation of the EKF algorithm offers a computationally efficient option for runtime SOC evaluation on-board vehicles. The SOC estimation was validated with experimental data of a current profile contaminated with pseudo-random noise and with an offset in the initial condition. The model rapidly converged to within 4% of the true SOC even with imposed errors of 40% to initial SOC, 24% to current measurement and 6% to voltage measurement.