A review of the state of health for lithium-ion batteries: Research status and suggestions

Tian, Huixin; Qin, Pengliang; Li, Kun; Zhao, Zhen

doi:10.1016/j.jclepro.2020.120813

Cited by 449 publications

(117 citation statements)

References 82 publications

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“…Finally, according to Equations (19) and (21), the terminal voltage in Equation (13) can be calculated by:…”

Section: Of 21mentioning

confidence: 99%

“…Many kinds of empirical models have been proposed using different techniques, such as statistical analysis [10,11], support vector [12], big data [13,14], and deep neural networks [15,16]. The empirical battery models mainly deal with the battery properties that related to capacity [17], State-of-Charge [18] and State-of-Health [19,20]. When using empirical-based approaches, researchers do not need to have a deep understanding of the electrochemical processes, which makes the empirical models easily implemented in practice.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

Yang

Liu

et al. 2020

Energies

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The lithium-ion battery is a complicated non-linear system with multi electrochemical processes including mass and charge conservations as well as electrochemical kinetics. The calculation process of the electrochemical model depends on an in-depth understanding of the physicochemical characteristics and parameters, which can be costly and time-consuming. We investigated the electrochemical modeling, reduction, and identification methods of the lithium-ion battery from the electrode-level to the system-level. A reduced 9th order linear model was proposed using electrode-level physicochemical modeling and the cell-level mathematical reduction method. The data-driven predictor-based subspace identification algorithm was presented for the estimation of lithium-ion battery model in the system-level. The effectiveness of the proposed modeling and identification methods was validated in an experimental study based on LiFePO4 cells. The accuracy and dynamic characteristics of the identified model were found to be much more likely related to the operating State of Charge (SOC) range. Experimental results showed that the proposed methods perform well with high precision and good robustness in the SOC range of 90% to 10%, and the tracking error increases significantly within higher (100–90%) or lower (10–0%) SOC ranges. Moreover, to achieve an optimal balance between high-precision and low complexity, statistical analysis revealed that the 6th, 3rd, and 5th order battery model is the optimal choice in the SOC range of 90% to 100%, 90% to 10%, and 10% to 0%, respectively.

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“…Finally, according to Equations (19) and (21), the terminal voltage in Equation (13) can be calculated by:…”

Section: Of 21mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

Yang

Liu

et al. 2020

Energies

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“…The overall functions of a BMS are to guarantee the safety and stability of the lithium-ion batteries (LiBs) and perform as a connector to exchange information between the LiBs and other control units of EVs among which the most important information is the battery states. [1][2][3][4] An accurate observation of battery states is a fundamental task for BMSs among which state of charge (SoC) is the most important state indicator. Moreover, an accurate SoC estimation is also the basis for the observation of other state indices, such as state of health (SoH), state of power (SoP), state of energy (SoE), and remaining discharge time (RDT).…”

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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“…Model-based method uses complex mathematical equations to simulate degradation mechanisms, necessitating a deeper understanding of electrochemical principles. Recently, various filtering models with model-based methods are applied in the previous research about the SOH prediction 9 , 10 . For example, Mejdoubi et al 11 proposed a particle filter (PF) model to estimate the SOH of SCs, in which the capacitance and resistance could be predicted accurately under various conditions by considering the aging temperature and voltage.…”

Section: Introductionmentioning

confidence: 99%

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression

Ren

Cai

2021

Sci Rep

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State of health (SOH) prediction of supercapacitors aims to provide reliable lifetime control and avoid system failure. Gaussian process regression (GPR) has emerged for SOH prediction because of its capability of capturing nonlinear relationships between features, and tracking SOH attenuations effectively. However, traditional GPR methods based on explicit functions require multiple screenings of optimal mean and covariance functions, which results in data scarcity and increased time consumption. In this study, we propose a GPR-implicit function learning, which is a prior knowledge algorithm for calculating mean and covariance functions from a preliminary data set instead of screening. After introducing the implicit function, the average root mean square error (Average RMSE) is 0.0056 F and the average mean absolute percent error (Average MAPE) is 0.6%, where only the first 5% of the data are trained to predict the remaining 95% of the cycles, thereby decreasing the error by more than three times than previous studies. Furthermore, less cycles (i.e., 1%) are trained while still obtaining low prediction errors (i.e., Average RMSE is 0.0094 F and Average MAPE is 1.01%). This work highlights the strength of GPR-implicit function model for SOH prediction of energy storage devices with high precision and limited property data.

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A review of the state of health for lithium-ion batteries: Research status and suggestions

Cited by 449 publications

References 82 publications

Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery

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

High precision implicit function learning for forecasting supercapacitor state of health based on Gaussian process regression

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