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

Abstract

The battery performance decreases as the charging/discharging cycles increase. Thus, a battery management system (BMS) is essential to properly estimating the battery states. In order to enhance the performance of the BMS, an accurate estimation method for lithium-ion batteries state is proposed. The main drawback of the coulomb counting method (CCM) for estimating a state of charge (SoC) is the error of initial value. To make-up this problem, the open circuit voltage (OCV) method which includes the internal resistance of the battery has been applied to update the initial value. In this paper, an enhanced coulomb counting (ECC) method is proposed to improve the accuracy of SoC estimation. Due to the battery aging by repeated charging/discharging cycles, the charging/discharging times become reduced and it can be formulated as a function of coulombic efficiency. Using the power equation to the battery, the state of health (SoH) can be estimated according to the change in the internal resistance. In the proposed flowchart, after the completion of charging/discharging in the k cycle, the internal resistance, coulombic efficiency, and capacities are calculated and those resultants will be utilized in k+1 cycle. The proposed methods are verified by 3

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

“…When the initial SoC value is known, this method works more efficiently considering a short time-period. Although this method has been widely used in recent years, CC is not actually used as a sole tool for estimating SoC but rather is used in combination with other techniques, e.g., in [49], the CC method is coupled with OCV to enhance the initial SoC estimation considering the effect of the internal resistance of the battery. Nonetheless, the easy implementation of the CC approach is not suitable for online SoC estimation [50].…”

Lithium Battery SOC Estimation Based on Multi-Head Attention Mechanism and GRU Algorithm

“…The lithium-ion battery is used as an analogous circuit or electrochemical model in the approach for estimating SOC that is based on models. Choi Woosung models [21] the lithium-ion battery with the EIS approach. Yang Fangfang [22] offers a fresh concept by combining UKF and LSTM.…”

Quantitative Ultrasound Spectroscopy for Screening Cylindrical Lithium‐Ion Batteries for Second‐Life Applications

“…Nonetheless, accurate SOH estimation is required to ensure the continued safe and correct operation of the SLBs, [14,21] and the methods to achieve this need to be relatively fast [22] . Standard capacity measurements for SOH estimation, using techniques such as Coulomb counting, are time‐consuming due to the low applied currents, and are therefore impractical for use in high throughput scenarios [23] . A challenge also arises from the absence of historical usage data, requiring additional tests to characterize the LIBs to account for the different degradation pathways that they might have undergone during their first life [24,25] .…”

“…[22] Standard capacity measurements for SOH estimation, using techniques such as Coulomb counting, are time-consuming due to the low applied currents, and are therefore impractical for use in high throughput scenarios. [23] A challenge also arises from the absence of historical usage data, requiring additional tests to characterize the LIBs to account for the different degradation pathways that they might have undergone during their first life. [24,25] Recent research has focused on developing rapid and non-destructive evaluation (NDE) techniques for battery state estimation, such as X-ray Computed Tomography (X-ray CT), [26][27][28] Electrochemical Impedance Spectroscopy (EIS) [29][30][31][32] and acoustic methods.…”