# Research of Adaptive Extended Kalman Filter-Based SOC Estimator for Frequency Regulation ESS
###### Abstract  
To achieve frequency regulation, energy-storage systems (ESSs) are systems that monitor and maintain the grid frequency. In South Korea, the total installed capacity of battery ESSs (BESSs) is 376 MW, and these have been employed to achieve frequency regulation since 2015. When the frequency of a power grid is input, accurately estimating the state of charge (SOC) of a battery is difficult because it charges or discharges quickly according to the frequency regulation algorithm. If the SOC of a battery cannot be estimated, the battery can be used in either a high SOC or low SOC. This makes the battery unstable and reduces the safety of the ESS system. Therefore, it is important to precisely estimate the SOC. This paper proposes a technique to estimate the SOC in the test pattern of a frequency regulation ESS using extended Kalman filters. In addition, unlike the conventional extended Kalman filter input with a fixed-error covariance, the SOC is estimated using an adaptive extended Kalman filter (AEKF) whose error covariance is updated according to the input data. Noise is likely to exist in the environment of frequency regulation ESSs, and this makes battery-state estimation more difficult. Therefore, significant noise has been added to the frequency regulation test pattern, and this study compares and verifies the estimation performance of the proposed AEKF and a conventional extended Kalman filter using measurement data with severe noise. b 2019 9 4274 10.3390/app9204274 10.  
through the ESS at light-duty hours, and which discharges the ESS during heavy-duty hours to reduce power costs [2]. The reference frequency of power systems globally is either 50 Hz or 60 Hz, depending on the country, and the line frequency varies according to the load usage. All electronic devices are manufactured to operate at the reference frequency of 50/60 Hz, so if the line frequency is not maintained at the reference frequency, it may deteriorate the product quality and efficiency [4; 5; 6; 7]. The frequency of the power system continues to fluctuate according to the power demand, and the system generation unit adjusts its output to maintain the line frequency. The frequency regulation (FR) ESS is a highly economical operation method because it can partially take the role of a generator that operates for frequency maintaining purposes, thereby reducing the operating cost of the generator [8; 9; 10; 11; 12]. When a BESS is used for frequency regulation, it reacts momentarily to sudden frequency drops due to the transient state or micro demand fluctuation, so it can contribute effectively to the stabilization of the line frequency [13; 14; 15; 16]. In this study, an algorithm is designed to analyze operation characteristics when the ESS was used for frequency regulation, and a power pattern is generated and analyzed. The accurate identification of the BESS battery's state of charge (SOC) is critical to ensuring system safety and reliability. When the SOC of a lithium-ion battery is at a high level, lithium ions that lose electrons owing to the oxidation reaction at the cathode electrode are in the state of being reduced to the maximum at the anode electrode. A low SOC indicates that the oxidation reaction occurs at the anode electrode, and the lithium ion moves to the cathode and the reduction reaction occurs [17; 18]. At this time, when the battery is used at an SOC that is too high or too low, the battery may encounter stress due to structural instability, and an instantaneous externally generated current may result in over-voltage or over-discharge conditions [19; 20]. In addition, because the life of the battery can vary widely depending on the SOC operating area of the battery, it is very important to accurately estimate the SOC and manage the battery in the correct SOC area in order to improve the life of the BESS.