State-of-charge estimation of batteries using the extended Kalman filter: insights into performance analysis and filter tuning

Document Type

Conference Proceeding

Publication Date

1-1-2022

Publication Title

1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

Keywords

coulomb counting, extended Kalman filter, open-circuit voltage, performance bounds, state-of-charge estimation

Abstract

Accurate state-of-charge (SOC) estimation is an essential part of management systems in rechargeable batteries. Traditionally, the SOC is estimated based on sensory measurements of current, voltage, or both. The current and voltage-based approaches are, in general, susceptible to several uncertainties as well as practical limitations. Meanwhile, the fusion of both approaches using nonlinear filters like the extended Kalman filter (EKF) tends to preserve the respective benefits and results in accurate SOC estimation. Existing research on SOC algorithms mainly concentrates on estimation accuracy and computational complexities. Very few formalize the theoretical limitations of the achievable estimation accuracy. This paper presents the derivations to quantify the exact theoretical estimation errors of the traditional SOC estimation approaches in the presence of measurement uncertainties. Through simulation analysis, we show the following: cumulative error characteristics of the current-based SOC, functional error dependence of the voltage-based SOC, and suppression of the cumulative and functiondependent errors by the fusion-based SOC. It also provides a comparative analysis of the EKF against the best possible performance that can be achieved for a given system model and set of parameters.

DOI

10.1109/ONCON56984.2022.10126605

ISBN

9798350398069

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