Comparative Study of the Influence of Open Circuit Voltage Tests on State of Charge Online Estimation for Lithium-Ion Batteries

Li, Yuan; Guo, Huiping; Qi, Fei; Guo, Zhiwei; Li, Meiying

doi:10.1109/access.2020.2967563

Cited by 50 publications

(31 citation statements)

References 44 publications

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“…Due to the limitation of present commercial battery techniques, some battery manufacturers suggest charging the battery with the current rate of less than 1 C (e.g., 1/3 C) to ensure the safety of battery systems. The current rate range from 1/3 C to 1 C is widely used in applications, literature, and some standard battery test manuals [4,8,20,21,[37][38][39]. Thus, the experiments are mainly conducted at the current rate range from 1/3 C to 1 C. The test data, including the loading current, and charge or discharge capacity, are recorded by a battery charger and stored in a host computer.…”

Section: Resultsmentioning

confidence: 99%

“…By substituting (18) and 20 (27) Substituting (19) and (21) into (27), one can obtain the relationship between…”

Section: A Feedback Voltage Error and Lithium-ion Concentration Corrmentioning

confidence: 99%

“…voltage (OCV) [20,21], electromotive force [22], internal resistance [23], and impedance spectroscopy [24]. Though generally simple and readily implementable, they have great strict requirements for experimental conditions and time.…”

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confidence: 99%

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An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

2020

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Lithium-ion batteries are generally regarded as a leading candidate for energy storage systems. The safe and reliable operation of lithium-ion batteries depends largely on accurate estimation of the state of charge (SOC), which requires an accurate battery model. Bearing strong mechanisms, the electrochemical model (EM) can mimic the battery dynamics with high fidelity, and thus the EM-based methods can produce more reliable SOC estimates. This paper proposes a novel EM-based SOC estimation method for lithium-ion batteries from the electrochemical mechanism perspective. Firstly, a single particle model is employed to gain a direct insight into the electrochemical reactions inside the battery, and it is found that the model output voltage and SOC are strongly related to the lithium-ion concentrations of solid phases. A simple negative voltage feedback module is then applied to observe the voltage error between the cell referenced terminal voltage and the model output voltage. To eliminate the voltage error and achieve a precise estimate, a quantitative relationship between the voltage error and corrected amount of lithium-ion concentrations is deduced based on the Nernst equation. The performance of proposed method has been systemically evaluated under different operating conditions, including various charging and discharging current rates, erroneous initial SOCs, and cell aging levels. Although an erroneous initial SOC of 50% is applied to the proposed algorithm, promising estimates with the mean absolute errors of 0.22% and 1.35% can be still achieved under the constant and dynamic loading conditions, respectively. INDEX TERMS Lithium-ion battery, state of charge (SOC), electrochemical model, Nernst equation I. INTRODUCTION With numerous advantages, such as high energy density, low self-discharging rate, no memory effects, and long lifespan, and so on, lithium-ion batteries are generally regarded as a leading energy storage candidate for electric vehicles, renewable energy systems, portable electronic devices, and many other applications [1-3]. In most lithium-ion battery energy storage systems, a battery management system (BMS) is employed to manage the performance and maintain the longevity of battery cells [1, 4, 5]. The functions of a BMS include mainly cell voltage detection and balancing [6, 7], estimation of state of charge (SOC) [8-11] and state of health (SOH) [12-14], and thermal management [15-18]. Defined as the ratio of the residual charge stored in a battery to its maximum available capacity, the SOC is one of the primary and critical parameters for the development of battery management strategies [19]. Accurate SOC estimation plays a vital role in controlling the safe charging and discharging of the battery while guaranteeing its efficiency. However, the SOC cannot be measured directly and accurately by sensors like ordinary physical quantities in practices. Despite the huge research efforts, the battery SOC estimation still poses a significant challenge due to the complexity and nonlinearity of e...

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Section: Resultsmentioning

confidence: 99%

“…By substituting (18) and 20 (27) Substituting (19) and (21) into (27), one can obtain the relationship between…”

Section: A Feedback Voltage Error and Lithium-ion Concentration Corrmentioning

confidence: 99%

See 1 more Smart Citation

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

2020

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“…25 For EECMs, the parameters needed to be identified consist of two parts: (a) the relationship between OCV and SoC and (b) the Ohmic resistance and the resistancecapacitance (RC) parallel network. The commonly used PIM methods to identify the Ohmic resistance and RC parallel network include least squares (LS) method, 19,24 recursive LS method with a forgetting factor (FFRLS), 26,27 KF family algorithms 28,29 and H-∞ algorithm. 30 For the relationship between OCV and SoC, the most commonly used method is to conduct a specific OCV test and analyze the experimental data to obtain an offline OCV-SoC curve.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

Ren

et al. 2021

Int J Energy Res

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A predetermined relationship between open circuit voltage (OCV) and state of charge (SoC) is the premise of model-based SoC estimation algorithms. Commonly used OCV tests to obtain the offline OCV-SoC curves include the low current OCV test and the incremental OCV test. In this paper, in addition to comparing the low current OCV test with the incremental OCV test which applies 0.5 C current-rate, the incremental OCV tests which apply 0.2, 0.3, and

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“…7 Although this method is simple and direct, it is an open-loop method, and the continuous accumulation of measurement errors will make the estimation results increasingly deviant 8 ; (b) The opencircuit voltage method, the principle of which is to fit the curve by measuring the open-circuit voltage (OCV) value corresponding to each SOE point to obtain the function expression, 9 and finally find the SOE value according to the OCV. Although this method has a high estimation accuracy, it requires a lot of time and energy for measurement 10 to accurately express the relationship between SOE and OCV, and cannot be applied to the online estimation of SOE of Li-ion batteries; (c) The pure data-driven method based on intelligent algorithms such as neural networks, which is trained by a large amount of historical data to simulate the dynamic characteristics of the battery and then estimate SOE. 1 Reference 11 used a reverse neural network for direct SOE estimation, which simplifies the complex mechanism caused by the electrochemical reaction process and has good robustness under dynamic temperature and operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Estimation of state‐of‐energy for lithium batteries based on dual adaptive particle filters considering variable current and noise effects

Zhang

Mao

et al. 2021

Int J Energy Res

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Accurate estimation of the state-of-energy (SOE) of lithium batteries is of great significance for the stable operation of electric vehicles. However, the timevarying parameters of the equivalent model and the difficulty of describing the nonlinear characteristics between the open circuit voltage (OCV) and SOE limit the further improvement of SOE estimation accuracy. In this study, the fitting method of OCV-SOE curves is improved and dual adaptive particle filters are used to improve the estimation accuracy of SOE. To improve the accuracy of the OCV-SOE correspondence expression at the beginning and end of the discharge process, the curve fitting is accomplished using Gaussian polynomials, and the model equations based on the Gaussian function system are reestablished. The identification of the equivalent model parameters and SOE estimation are achieved simultaneously using two interrelated particle filters, which partially weaken the influence of the time-varying equivalent parameters on the SOE estimation accuracy. To further improve the convergence capability of the algorithm, a noise adaptation method based on the change of state estimation is added to the dual particle filters so that the noise variance meets the requirements of the algorithm throughout the discharge process. The test results of two different discharge conditions, dynamic stress test, and supplemental federal test procedure, verify the effectiveness of the proposed method.

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Comparative Study of the Influence of Open Circuit Voltage Tests on State of Charge Online Estimation for Lithium-Ion Batteries

Cited by 50 publications

References 44 publications

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

Estimation of state‐of‐energy for lithium batteries based on dual adaptive particle filters considering variable current and noise effects

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