An SOC estimation method based on sliding mode observer and the Nernst Equation

Chang, Fengqi; Zheng, Zedong

doi:10.1109/ecce.2015.7310527

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…By adapting the Walcott-Zak observer (Chang and Zheng, 2015), the sliding mode observer of the HESS is established: where d 1 −d 6 is used to simulate the influence of system noise.…”

Section: Design Of Sliding Mode Observermentioning

confidence: 99%

Research on Dynamic Equivalent SOC Estimation of Hybrid Energy Storage System Based on Sliding Mode Observer

et al. 2021

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This article proposes a sliding mode observer based dynamic equivalent state of charge (ESOC) estimation method for hybrid energy storage system (HESS). Since different types of energy storage components and power electronics circuit are coupled in the HESS, the traditional SOC estimation method cannot reflect the real-time operation characteristics of the HESS. To tackle this problem, a sliding mode observer based on the model of the HESS is built in this article. By collecting the corresponding voltage and current signals, the internal parameters of energy storage elements can be observed accurately in real time. The dynamic ESOC is further defined with the idea of real-time charge balance to reflect the accurate available capacity of the HESS. Finally, the simulation results based on MATLAB/Simulink model are given to verify the feasibility of the proposed dynamic ESOC.

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“…By adapting the Walcott-Zak observer (Chang and Zheng, 2015), the sliding mode observer of the HESS is established: where d 1 −d 6 is used to simulate the influence of system noise.…”

Section: Design Of Sliding Mode Observermentioning

confidence: 99%

Research on Dynamic Equivalent SOC Estimation of Hybrid Energy Storage System Based on Sliding Mode Observer

et al. 2021

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“…Nernst equation is an electrochemical expression of the battery electromotive force based on reversible chemical reaction. The Nernst equation can be expressed as

E = E_{0} - \frac{R T_{K}}{n F} normalln \frac{α_{red}}{α_{ox}}

where T k is the thermodynamics temperature of chemical reaction; E is the electrode potential under T k , and it is also the open circuit voltage of the battery; E 0 is the standard electrode potential, which can be treated as the open circuit voltage when the battery is fully charged; R is the gas constant 8.314 J · K −1 · mol −1 ; F is the Faraday constant and equals 96485 C/mol; n is the number of the electrons that participate in the electrode reaction; α ox and α red are the ion activities of the high and low oxidized side, respectively . The oxidation‐reduction reaction of the Li + is expressed as

{normalL normali}^{+} + e^{-} \Leftrightarrow normalL normali

can be rewritten as

E = E_{0} + \frac{R T_{K}}{F} normalln \frac{α_{{normalL normali}^{+}}}{α_{L i}}

…”

Section: Multiple Input Battery Modelmentioning

confidence: 99%

“…where the function f(x, i) and g(x, i) are the observation equation (see (7)) and state equation (see (15)), respectively. The steps of EKF algorithm are given as follows:…”

Section: State-of-charge Estimation Based On Ekf Algorithmmentioning

confidence: 99%

“…For this type of model, the relationship between system parameters is clear, and the parameters are easy to identify. Most of the existing electrochemistry models are based on the Nernst equation . However, the influence of temperature is rarely taken into consideration, and there is a need to introduce a temperature compensation coefficient to improve the accuracy of the battery model.…”

Section: Introductionmentioning

confidence: 99%

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A new state-of-charge estimation method for electric vehicle lithium-ion batteries based on multiple input parameter fitting model

Liu

Zheng

et al. 2017

Int. J. Energy Res.

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Summary The estimation of state‐of‐charge (SOC) is crucial to determine the remaining capacity of the Lithium‐Ion battery, and thus plays an important role in many electric vehicle control and energy storage management problems. The accuracy of the estimated SOC depends mostly on the accuracy of the battery model, which is mainly affected by factors like temperature, State of Health (SOH), and chemical reactions. Also many characteristic parameters of the battery cell, such as the output voltage, the internal resistance and so on, have close relations with SOC. Battery models are often identified by a large amount of experiments under different SOCs and temperatures. To resolve this difficulty and also improve modeling accuracy, a multiple input parameter fitting model of the Lithium‐Ion battery and the factors that would affect the accuracy of the battery model are derived from the Nernst equation in this paper. Statistics theory is applied to obtain a more accurate battery model while using less measurement data. The relevant parameters can be calculated by data fitting through measurement on factors like continuously changing temperatures. From the obtained battery model, Extended Kalman Filter algorithm is applied to estimate the SOC. Finally, simulation and experimental results are given to illustrate the advantage of the proposed SOC estimation method. It is found that the proposed SOC estimation method always satisfies the precision requirement in the relevant Standards under different environmental temperatures. Particularly, the SOC estimation accuracy can be improved by 14% under low temperatures below 0 °C compared with existing methods. Copyright © 2017 John Wiley & Sons, Ltd.

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“…Combining the Nernst model with other battery models such as the equivalent circuit model and the Shepherd model enhances the accuracy of the representation of the battery's behavior. The Nernst model [122][123][124][125] serves as a valuable tool for predicting the OCV of a battery based on its SOC and temperature. When appropriately calibrated, this model can swiftly and accurately predict real battery performance and help with finding applications for optimizing battery management strategies and estimating the remaining useful life of a battery.…”

mentioning

confidence: 99%

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

Dini,

Colicelli,

Saponara

2024

Batteries

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Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation.

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An SOC estimation method based on sliding mode observer and the Nernst Equation

Cited by 8 publications

References 8 publications

Research on Dynamic Equivalent SOC Estimation of Hybrid Energy Storage System Based on Sliding Mode Observer

Research on Dynamic Equivalent SOC Estimation of Hybrid Energy Storage System Based on Sliding Mode Observer

A new state-of-charge estimation method for electric vehicle lithium-ion batteries based on multiple input parameter fitting model

Review on Modeling and SOC/SOH Estimation of Batteries for Automotive Applications

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