A health indicator extraction and optimization for capacity estimation of Li-ion battery using incremental capacity curves

Pan, Wenjie; Luo, Xuesong; Zhu, Ming; Jia, Yali; Guo, Lihong; Qu, Hengjun

doi:10.1016/j.est.2021.103072

Cited by 50 publications

(16 citation statements)

References 57 publications

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“…To retain focus on the qualitative graphical methods advocated in this work, we have not attempted this exercise on the small quantity of data presented here. For further detail, we direct the reader to published reports on fitting and numerical regression methods introduced for this purpose; ,,,,− context is given by a recent review, and we note a significantly increased number of subsequent publications on this topic during 2021–2022.…”

Section: Differential Analysis In Practice: Case Study and Recommenda...mentioning

confidence: 99%

Differential Analysis of Galvanostatic Cycle Data from Li-Ion Batteries: Interpretative Insights and Graphical Heuristics

2023

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Differentiation of a Li-ion battery cycling profile (galvanostatic voltage vs charge) yields a pair of complementary measures: differential capacity (dQ/dV vs voltage, also called incremental capacity) and differential voltage (dV/dQ vs charge). These metrics, especially when obtained under experimental conditions approximating cell equilibrium, are widely used to diagnose cell state and mechanisms related to cell aging. Central to the resulting diagnosis is the understanding of how the chemistry of individual electrode materials manifests in the full-cell differential plots. In this article, a fundamental theory of differential analysis is advanced from the starting point of idealized single electrode (half-cell) measurements, building up to the nontrivial translation to full cell data, with consideration of cell balancing and degradation. Various complementary approaches from the existing literature are harmonized with the aid of a novel graphical heuristic in which cell voltage and differential capacity data are superimposed on a 2D representation (“square plot”) of the cell thermodynamic state. Interpretative analysis based on the new approach is demonstrated using data from a commercial pouch cell with the well-characterized LiCoO2|graphite chemistry. A brief review of current literature on practical uses of differential analysis is also provided.

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Section: Differential Analysis In Practice: Case Study and Recommenda...mentioning

confidence: 99%

Differential Analysis of Galvanostatic Cycle Data from Li-Ion Batteries: Interpretative Insights and Graphical Heuristics

2023

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“…In the case of constant current charge and discharge, the IC curve is related to the derivative (dV/dt) of the voltage-time curve, as shown in Equation (2). Therefore, the capacity increment curve can convert the voltage plateau that is difficult to observe and analyze with slow voltage changes during the charging process of the battery into a peak value on the capacity increment curve that is easy to observe and analyze [67,68]. The battery IC curve is shown in Figure 6.…”

Section: Indirect Hi Based On Charging Processmentioning

confidence: 99%

Review on the Selection of Health Indicator for Lithium Ion Batteries

Zhou

Zheng

2022

Machines

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Scientifically and accurately predicting the state of health (SOH) and remaining useful life (RUL) of batteries is the key technology of automotive battery management systems. The selection of the health indicator (HI) that characterizes battery aging affects the accuracy of the prediction model construction, which in turn affects the accuracy of SOH and RUL estimation. Therefore, this paper analyzes the current status of HI selection for lithium-ion batteries by systematically reviewing the existing literature on the selection of HIs. According to the relationship between HI and battery aging, battery HI can be divided into two categories: direct HI and indirect HI. The capacity and internal resistance of the battery can directly represent the aging degree of the battery and are the direct HIs of the battery. Indirect HIs refer to characteristic parameters extracted from battery charge and discharge data that can characterize the degree of battery aging. This paper analyzes and summarizes the advantages and disadvantages of various HIs and indirect HIs commonly used in current research, providing useful support and reference for future researchers in selecting HIs to characterize battery aging. Finally, in view of the capacity regeneration phenomenon in the aging process of the battery, the selection direction of future HI is proposed.

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“…Li et al 29 proposed a novel hybrid method by fusion of partial incremental capacity and Gaussian process regression, and used dual Gaussian process regression models to predict battery health conditions. Pan et al 30 used incremental capacity curves to extract and optimize health indicators (HI), and based on these HI, applied an improved Gaussian process regression model to estimate the battery capacity.…”

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confidence: 99%

A Prediction Framework for State of Health of Lithium-Ion Batteries Based on Improved Support Vector Regression

Qiang,

Zhang,

Ding

2023

J. Electrochem. Soc.

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As one crucial function of a battery management system (BMS), the state of health (SOH) prediction of a lithium-ion battery is of great significance to safe system operation and the battery’s service life. This paper proposes a framework for SOH prediction, which includes the feature points extraction and SOH prediction. First, based on the incremental capacity (IC) curve, the improved incremental capacity (IIC) curve is deduced by taking the derivative of the IC curve, and the grey relational analysis (GRA) is adopted to select the four feature points with the highest grey relational grade (GRG). Then, an improved sparrow search algorithm (ISSA) is proposed to optimize the parameters of the support vector regression (SVR) for more precise SOH prediction. Finally, experiments are carried out and the results show that the proposed feature points extraction method based on the IC/IIC curves and GRA is efficiently to improve the SOH prediction accuracy. Furthermore, compared with three traditional algorithms, the ISSA-SVR can restrict the SOH prediction error within 1.7%, and it also shows the proposed SOH prediction framework has strong robustness and high universality.

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A health indicator extraction and optimization for capacity estimation of Li-ion battery using incremental capacity curves

Cited by 50 publications

References 57 publications

Differential Analysis of Galvanostatic Cycle Data from Li-Ion Batteries: Interpretative Insights and Graphical Heuristics

Differential Analysis of Galvanostatic Cycle Data from Li-Ion Batteries: Interpretative Insights and Graphical Heuristics

Review on the Selection of Health Indicator for Lithium Ion Batteries

A Prediction Framework for State of Health of Lithium-Ion Batteries Based on Improved Support Vector Regression

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