Combined RLS-ΕKF Method for Simultaneous SOC and Parameter Estimations for Lithium-Ion Batteries

Abstract

This paper is concerned with the State-Of-Charge (SOC) estimation for lithium-ion batteries based an equivalent circuit model. A method for simultaneous estimation of the SOC and circuit parameters is presented by combining the extended Kalman filter (EKF) and the recursive least squares (RLS) estimation methods. The estimation accuracy of the present method is assessed through a discharge experiment of a LiFePO 4 battery. In particular, the AC impedance analysis using the Cole-Cole plots shows that the parameter estimates obtained by the RLS part well describe the behavior of the battery in the operating frequency range. This supports the effectiveness of the present method.

Li-ion Batteries Parameter Estimation Using Converter Excitation and Fusion Methods

“…According to (1), the impedance consists of two poles and the E component in (5) is 0, resulting in the transfer function in (6). The coefficients in (6) are related with the battery equivalent circuit parameters as stated in (7)(8)(9)(10)(11).…”

“…This paper presents a novel fusion method which initializes the parameters with a frequency method known as vector fitting (VF), and uses the recursive least squares (RLS) algorithm for both estimating the states and the parameters in real time. Furthermore, a more accurate model which consists of two RC branches is considered, rather than the single RC model often used [10], [11].…”

SOC Estimation of Lithium-Ion Battery by Extended Kalman Fixed Lag Smoother

“…In order to resolve this problem, the model-based SOC estimation technique using the extended Kalman filter (EKF) was proposed by Plett [1], and has been extensively studied for the last decade (see e.g. [2], [3]). The EKF recursively computes the sub-optimal estimate of the SOC based on the current and voltage measurements up to the current time.…”