An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery

Meng, Jinhao; Ricco, Mattia; Luo, Guangzhao; Świerczyński, Maciej; Stroe, Daniel‐Ioan; Stroe, Ana-Irina; Teodorescu, Remus

doi:10.1109/tia.2017.2775179

Cited by 248 publications

(80 citation statements)

References 62 publications

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“…Generally, the SoC of cells can be estimated with OCV(SoC)-based method [29,30], with power electronics such as online EIS measurement [31,32], model-based-estimation and machine learning algorithms, and Ah(Coulomb)-counting, as it thoughtfully discussed in previous studies [33][34][35][36]. Challenges with certain chemistries can rise, as for LFP's low ∆OCV/∆SoC at the slow dynamic area and its hysteresis effect makes OCV (SoC)-based methods not optimal [37,38], whereas EIS-based measurements lack accuracy through ageing and the estimations are highly influenced from chemistry and experimental conditions [39]. Also, due to overload on computational complexity and memory storage or lack of accuracy, most implementation are to not suitable for on-board applications.…”

Section: In Discrete-time Domainmentioning

confidence: 99%

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

et al. 2019

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Md.Sazzad.Hosen@vub.be (M.S.H.); Mohsen.Akbarzadeh.Sokkeh@vub.be (M.A.S.); Shovon.Goutam@vub.be (S.G); Joris.Jaguemont@vub.be (J.J.); Maitane.Berecibar@vub.be (M.B.); Joeri.van.mierlo@vub.be (J.V.M.) 2 Flanders Make,Abstract: A lithium-ion battery cell's electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light-and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs' parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

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Section: In Discrete-time Domainmentioning

confidence: 99%

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

et al. 2019

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“…Many different methods for the state estimation have been proposed in the literature. Frequently-mentioned methods for SOC estimation are: ampere-hour counting, open-circuit voltage (OCV) based, model based, impedance based, static battery characteristics based, fuzzy logic and machine learning based estimations [8]. Each of these methods has its advantages and drawbacks, which makes them suitable for different battery technologies.…”

Section: State-of-charge and State-of-health Estimationmentioning

confidence: 99%

Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries

et al. 2018

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Lithium-sulfur (Li-S) batteries are an emerging energy storage technology with higher performance than lithium-ion batteries in terms of specific capacity and energy density. However, several scientific and technological gaps need to be filled before Li-S batteries will penetrate the market at a large scale. One such gap, which is tackled in this paper, is represented by the estimation of state-of-health (SOH). Li-S batteries exhibit a complex behaviour due to their inherent mechanisms, which requires a special tailoring of the already literature-available state-of-charge (SOC) and SOH estimation algorithms. In this work, a model of SOH based on capacity fade and power fade has been proposed and incorporated in a state estimator using dual extended Kalman filters has been used to simultaneously estimate Li-S SOC and SOH. The dual extended Kalman filter’s internal estimates of equivalent circuit network parameters have also been used to the estimate maximum available power of the battery at any specified instant. The proposed estimators have been successfully applied to both fresh and aged Li-S pouch cells, showing that they can accurately track accurately the battery SOC, SOH, and power, providing that initial conditions are suitable. However, the estimation of the Li-S battery cells’ capacity fade is shown to be more complex, because the practical available capacity varies highly with the applied current rates and the dynamics of the mission profile.

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“…For example for an LiFePO4 battery cell, the dependency on SOC is taking place at low frequencies while SOH is normally analyzed at around few kilohertz where the impedance imaginary part is zero [1], [2]. Batteries are widely modeled by an equivalent-circuit-model (ECM) often regarded as Randles' model which is often used in SOC and SOH estimation algorithms [1], [3], [4]. Parameters of the ECM can be derived from the data of the complex impedance plot.…”

Section: Internal Impedance Of the Batterymentioning

confidence: 99%

Online identification of internal impedance of Li-ion battery cell using ternary-sequence injection

Sihvo

Messo

Roinila

et al. 2018

2018 IEEE Energy Conversion Congress and Exposition (ECCE)

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The internal impedance of a battery has been shown to change as a function of state-ofcharge (SOC) and state-of-health (SOH). Therefore, online impedance measurements can provide useful information for SOC-and SOH-estimation algorithms. Broadband injections techniques such as pseudo-random-binarysequence (PRBS) are attractive alternative for replacing conventional electrochemical-impedance-spectroscopy (EIS) which is very slow for online battery impedance measurements. However, non-linearities of batteries have a negative impact on the measurement results obtained by the PRBS for which the identified system is assumed to be linear. This paper demonstrates the use of ternarysequence excitation signal for battery impedance measurements. Appropriately designed ternary-sequence can efficiently capture the linear component of the impedance by canceling out the distortion caused by the non-linear parts. The presented method can be used for rapid impedance measurements and thus, as a valuable tool in online SOC-and SOH-estimation algorithms. Experimental measurements are shown from a Lithium-Iron-Phosphate (LiFePO4) battery cell.

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An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery

Cited by 248 publications

References 62 publications

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

Concurrent Real-Time Estimation of State of Health and Maximum Available Power in Lithium-Sulfur Batteries

Online identification of internal impedance of Li-ion battery cell using ternary-sequence injection

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