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A Method for Simultaneous State of Charge, Maximum Capacity and Resistance Estimation of a Li-Ion Cell Based on Equivalent Circuit Model
ISSN: 0148-7191, e-ISSN: 2688-3627
To be published on April 14, 2020 by SAE International in United States
Accurate estimation of the State of Charge (SOC), maximum capacity (Qmax) and internal resistance are critical for battery monitoring, i.e., determining the status, health, and performance figures of a battery. SOC is a key indicator of the instant status for battery systems, while Qmax and internal resistance are related to the capacity fade (SOHQ) and power fade (SOHP) respectively, which represent the abilities of a battery to store energy, retain charge over extended periods and provide the required power for acceleration, etc. Traditional methods using complex models and look-up tables have high computation requirements which makes them 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). A Variable Model framework based Adaptive Extended Kalman filter (VM-AEKF) is implemented for joint SOC and model parameter estimation where the VM framework is designed specifically to improve the stability and accuracy of parameter estimation under conditions when the system is not sufficiently excited by the input signal. Simultaneously, a forgetting-factor based Recursive Least Square (RLS) filter algorithm is implemented to estimate the slowly varying maximum capacity. 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. The proposed algorithms are validated offline and Battery-in-the-loop (BITL) for a large format NMC/Carbon pouch power cell using single and multiple charge-discharge cycles considering different temperature, 100 C to 500 C, and aging, Beginning-of-Life (BOL) to End-of-Life (EOL). The experimental results verify the improvement in parameter estimation with less than 5% SOC estimation error and less than 3% capacity estimation error for the typical SOC range of 10% to 90%.