Accurate estimation of the State of Charge (SOC), maximum capacity (Qmax) and internal resistance (R0) are essential for efficient battery monitoring, which is an important part of the battery management system. SOC provides information regarding the instantaneous status of the battery system, while Qmax is a key indicator of the long-term State of Health (SOH) of the cell, which represents the abilities of a battery to store energy and retain charge over extended periods. In addition, the internal resistance is also required to predict the peak available power.
Traditional methods use complex models and look-up tables that have high computation requirements and are thus unsuitable for online applications. In this paper, we propose a simple method for simultaneous SOC, Qmax and internal resistance estimation based on a second-order equivalent circuit model (ECM). An Adaptive Unscented Kalman filter (AUKF) is proposed for joint SOC and ECM parameter estimation along with a forgetting-factor based Recursive Least Square (RLS) filter algorithm to estimate the slow varying Qmax. The two methods are implemented together using a simple closed-loop framework, where the estimated values from one estimator are used to update the other and vice versa.
The proposed algorithms are validated for a large format NMC/Carbon pouch power cell using multiple charge-discharge cycles considering different aging conditions. The experimental results verify the proposed estimation approach with less than 5% SOC estimation error and less than 3% capacity estimation error for the typical SOC range of 10% to 90%.