Online State-of-Charge Estimation for Lithium-Ion Batteries Considering Model Inaccuracies Under Time-Varying Current Conditions

Abstract

Load currents of batteries undergo real-time change depending on the operations of battery-powered devices, such as acceleration, deceleration, and sudden braking of electric vehicles. With the variation in current magnitude, parameters in an equivalent circuit model of the battery change depending on its chemical characteristics. Therefore, accurately identifying model parameters offline for various sizes and patterns of load currents with few experiments is difficult. Besides, there is a model uncertainty due to the difference between the linearity of the model structure and the nonlinearity of the real battery; hence, an experimentally obtained open-circuit voltage (OCV)-state-of-charge (SOC) curve has difficulty in providing an accurate OCV value for the model. Consequently, these difficulties lead to inaccuracies in the model and SOC estimation. In this paper, novel methods are proposed to reduce these inaccuracies under timevarying current conditions, where the magnitude of the load current changes in real time. These are (1) an OCV-SOC curve estimation for reducing the model uncertainty and (2) a current-adaptive extended Kalman filter algorithm for decreasing the effect of inaccurately identified model parameters. The experimental results show that the maximum-absolute-errors of the proposed SOC estimation were reduced to <1.5%. Consequently, battery-powered applications can obtain highly accurate SOC values even the magnitude of the load current changes in real time. INDEX TERMS Battery management system, electric vehicles, open-circuit voltage-state-of-charge curve estimation, adaptive extended Kalman filter, state-of-charge estimation.

Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles

“…Figure 6 represents the scheme of the proposed online cell screening algorithm. The parameters required in the proposed algorithm are assumed to be provided as follows: i t and v t are on-board measurement parameters, and R s is calculated as the ohmic relation between the change in i t and v t [28][29][30][31][32][33][34]; C Ah is obtained through the online SOH estimation algorithm as described in [35][36][37][38]. OCV is obtained by feeding the online SOC estimation results into the SOC-to-OCV look-up table [39][40][41][42][43][44][45][46][47][48].…”

Enhanced Coulomb Counting Method for SoC and SoH Estimation Based on Coulombic Efficiency

“…Reviewed SoC estimation methods are classified into 4groups: direct measurement, book-keeping, model-based, and data-driven. First, an open circuit voltage (OCV) [10], [11], internal resistance (IR) [12], and impedance spectroscopy (IS) [13], [14] based estimation methods are investigated as a direct measurement category. Second, by measuring the current, a coulomb counting method (CCM) [15], [16] is categorized into book-keeping.…”

Direct Comparison using Coulomb Counting and Open Circuit Voltage Method for the State of Health Li-Po Battery

“…In addition, this function is also used to estimate SOH through the Coulomb Counting. The OCV-SOC function relates to battery modeling accuracy [45]. And to get the battery model parameters with the RLS algorithm.…”