Abstract
State-of-charge (SOC) estimation of lithium-ion batteries in portable devices without sensing the current is considered in this study. Unlike the traditional approach of separate estimation of the SOC and current, we firstly reformulate the problem as state estimation for the nonlinear system with an unknown input which refers to the current in this study, then a novel variational Bayes-based unscented Kalman filter (VB-UKF) is proposed to simultaneously estimate the SOC and the current input for the nonlinear lithium-ion battery system. Verifications of the SOC estimation performance are made by the experiments under the pulsed-discharge profile and urban dynamometer driving schedule profile. Experimental results show that the proposed VB-UKF algorithm is superior to the unscented recursive three-step filter (URTSF) in terms of convergence rate and estimation accuracy of the SOC and current. And the SOC root mean square errors of VB-UKF are bounded within ±3% after convergence which indicates the feasibility and effectiveness of the proposed method.
Highlights
R ECENTLY, lithium-ion batteries have experienced explosive growth for use in a wide range of applications, from portable devices to large-scale high-power energy storage systems [1]
The maximum errors appear during periods of transient current, but under the steady-state current condition, the modeling errors tend to be 0 mV. These findings indicate that this equivalent circuit models (ECMs) model is sufficient to simulate the electrical behaviour of the battery
These findings indicate that variational Bayes-based unscented Kalman filter (VB-unscented Kalman filter (UKF)) is more accurate and robust than unscented recursive three-step filter (URTSF) since its simultaneous estimation of SOC and current, which makes it better compensate the initial SOC error
Summary
R ECENTLY, lithium-ion batteries have experienced explosive growth for use in a wide range of applications, from portable devices to large-scale high-power energy storage systems [1]. Chun et al [29] extracted the estimated OCV and current information from the filtered terminal voltage, and calculate the battery SOC using the Ah integration method These methods either utilized a very simple battery model consisting of only a resistor and a capacitor, or adopted a linear relationship between OCV and SOC. The VB approach was used to approximate the joint posterior of the state and the input, and a recursive filtering algorithm combined with the UKF for nonlinear systems was derived It can achieve the simultaneous estimation of the SOC and current, and has a better SOC estimation performance than the URTSF under different operating conditions.
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