Abstract
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.
Highlights
In recent years, battery energy storage system (BESS) have been widely used in renewable energy (RE)-based power generation (REBPG) applications and microgrids to smooth the intermittent power generation, buffer the power generation–demand differences, and improve the frequency stability of power systems
Open-circuit voltage (OCV) methods [5,6,7] can be used to estimate battery state of charge (SOC) using the approximately proportional relationship between battery OCV and SOC. The key to this method is that when measuring the OCV, the battery must be disconnected from any load, and the distribution of internal electrolyte concentration must be uniform
To improve the estimation reliability, this paper proposes a hybrid real-time battery SOC estimation scheme for practical applications, where two detecting methods, adaptive Coulomb counting method and adaptive network-based fuzzy inference system (ANFIS)-based online estimating algorithm, are integrated to constitute a hybrid SOC estimation scheme
Summary
BESSs have been widely used in renewable energy (RE)-based power generation (REBPG) applications and microgrids to smooth the intermittent power generation, buffer the power generation–demand differences, and improve the frequency stability of power systems. Open-circuit voltage (OCV) methods [5,6,7] can be used to estimate battery SOC using the approximately proportional relationship between battery OCV and SOC The key to this method is that when measuring the OCV, the battery must be disconnected from any load, and the distribution of internal electrolyte concentration must be uniform. When battery SOC is around 20–10%, the terminal voltage tends to drop exponentially As a result, this method is not suitable for a BESS whose load is constantly changing. Similar to internal resistance method, before measuring it is necessary to let the battery rest and the electrolyte to distribute evenly after charging/discharging.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
