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24 ADI | Energy Storage Solutions SOC and SOH Estimation Methods Overview Regarding SOC and SOH estimation methods, three approaches mainly find employment: (1) a coulomb counting method, (2) voltage method, and (3) Kalman filter method. Coulomb Counting Method The coulomb counting method, also known as ampere-hour counting and current integration, is the most common technique for calculating the SOC. This method employs battery current readings mathematically integrated over the usage period to calculate SOC values given by Where SOC (t0) is the initial SOC, C rated is the rated capacity, I b is the battery current, and I loss is the current consumed by the loss reactions. The coulomb counting method then calculates the remaining capacity by merely accumulating the charge transferred in or out of the battery. The accuracy of this method resorts primarily to a precise measurement of the battery current and accurate estimation of the initial SOC. With a previously known capacity, which might be memorized or initially estimated by the operating conditions, the SOC of a battery can be calculated by integrating the charging and discharging currents over the operating periods. However, the releasable charge is always less than the stored charge in the charging and discharging cycle. In other words, there are losses during charging and discharging. These losses, also with the self- discharging, cause accumulating errors. For more precise SOC estimation, these factors should get consideration. Additionally, the SOC should get recalibrated regularly and the declination of the releasable capacity should get considered for a more precise estimate. Voltage Method The SOC of a battery, that is, its remaining capacity, can be determined using a discharge test under controlled conditions. The voltage method converts a reading of the battery voltage to the equivalent SOC value using the known discharge curve (voltage vs. SOC) of the battery. However, the voltage is more significantly affected by the battery current because of the battery's electrochemical kinetics and temperature. It is possible to make this method more accurate by compensating the voltage reading by a correction term proportional to the battery current and by using a lookup. Kalman filtering is an online and a dynamic method, and it needs a suitable model for the battery and precise identification of its parameters. It also requires a large computing capacity and an accurate initialization. Kalman Filter Method The Kalman filter is an algorithm to estimate the inner states of any dynamic system—it can also be used to determine the SOC of a battery. Kalman filters were introduced in 1960 to provide a recursive solution to optimal linear filtering for both state observation and prediction problems. Compared to other estimation approaches, the Kalman filter automatically provides dynamic error bounds on its state estimates. By modeling the battery system to include the wanted unknown quantities (such as SOC) in its state description, the Kalman filter estimates their values and gives error bounds on the estimates. It then becomes a model-based state estimation technique that employs an error correction mechanism to provide real-time predictions of the SOC. It can get extended to increase the capability of real-time SOH estimation using the extended Kalman filter. Notably, the extended Kalman filter is applied when the battery system is nonlinear, and a linearization step is needed. Kalman filtering is an online and a dynamic method, and it requires a suitable model for the battery and precise identification of its parameters. It also needs a large computing capacity and an accurate initialization. Other methods for SOC estimation are presented in various literature, such as impedance spectroscopy, based upon cell impedance measurements, employing an impedance analyzer in real-time for both charge and discharge. Although this technique can get used for