In order to maximize the operating flexibility and optimize the system performance of a battery energy storage system (BESS), developing a reliable real-time estimation method for the state of charge (SOC) of a BESS is one of the crucial tasks. In practice, the accuracy of real-time SOC detection can be interfered with by various factors, such as battery’s intrinsic nonlinearities, working current, temperature, and aging level, etc. Considering the feasibility in practical applications, this paper proposes a hybrid real-time SOC estimation scheme for BESSs based on an adaptive network-based fuzzy inference system (ANFIS) and Coulomb counting method, where a commercially available lead-acid battery-based BESS is used as the research target. The ANFIS allows effective learning of the nonlinear characteristics in charging and discharging processes of a battery. In addition, the Coulomb counting method with an efficiency adjusting mechanism is simultaneously used in the proposed scheme to provide a reference SOC for checking the system reliability. The proposed estimating scheme was first simulated in a Matlab software environment and then implemented with an experimental hardware setup, where an industrial-grade digital control system using DS1104 as the control kernel and dSPACE Real-Time Interface (RTI) interface were used. Results from both simulation and experimental tests verify the feasibility and effectiveness of the proposed hybrid SOC estimation algorithm.