A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter

Li, Yang; Abdel-Monem, Mohamed; Gopalakrishnan, Rahul; Berecibar, Maitane; Nanini-Maury, Elise; Omar, Noshin; Bossche, Peter Van den; Mierlo, Joeri Van

doi:10.1016/j.jpowsour.2017.10.092

Cited by 371 publications

(197 citation statements)

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“…By the definition of IC analysis in Equation (1), the differential voltage interval ∆V plays a critical role, given that a small ∆V may introduce errors due to measurement noise, while a large ∆V could result in a smoother IC curve but with the risk of losing key features [21]. There have been plenty of different methods proposed to obtain an accurate and smooth IC curve, such as the moving average filter [31], the Gaussian filter [24], and the improved center least squares method [30]. Detailed comparative study of these methods is not the focus of this paper and the filtfilt() from Matlab is selected due to its simplicity under comparable smoothing effect and its advantage in zero phase delay.…”

Section: Obtain the Incremental Capacity (Ic) Curvementioning

confidence: 99%

“…Immense research efforts have been dedicated into LIB aging assessment, in particular the SOH evaluation, which serves as a quantitative metric of aging and also as the foundation for predicting RUL. An extensive literature review indicates that state-of-the-art on LIB aging can be classified into three main categories: the model-based approach [6][7][8][9][10][11][12][13][14][15][16], the data-driven methods [17][18][19][20], and the experimental techniques [21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, data-driven methods are often subject to certain drawbacks, such as data saturation and specific input requirement. And the reliability of data-driven methods strongly depends on the scope of training data, which severely hinders their application in BMS.The experimental techniques [21][22][23][24][25][26][27][28][29][30][31] are based on electrochemical principles and can be grouped into ex-situ and in-situ methods. Common ex-situ methods, such as scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and X-ray diffractometry (XRD), apply physicochemical and electrochemical invasive techniques to study the battery cell internally.…”

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confidence: 99%

“…Specifically, IC analysis transforms voltage plateaus on the charging/discharging voltage-capacity (V-Q) curve into clearly identifiable dQ/dV peaks on the IC curve, by differentiating the charge capacity (Q) versus the voltage (V) [23]. The peaks on the IC curve can be associated with the phase transition during lithium intercalation and de-intercalation [24]. And the characteristics of extracted peak properties, such as position and amplitude, can be applied for battery health monitoring and aging assessment.However, the IC analysis is not without limitations.…”

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confidence: 99%

“…In addition, the voltage plateau of V-Q curve is flat and easily subject to measurement noise, rendering difficulty for extracting dQ/dV from raw data. Hence, the reported research on IC analysis for LIB health assessment are mainly focused on either the degradation mechanisms [26,27] or the design of robust algorithms to obtain an improved IC curve [23][24][25][28][29][30][31], often confined to a specific battery cell under a given load profile. There has been scarce published work on applying IC analysis to study impacts of diverse usage patterns on battery capacity fade, which is a realistic and highly challenging problem in practical automotive applications.…”

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confidence: 99%

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Incremental Capacity Analysis-Based Impact Study of Diverse Usage Patterns on Lithium-Ion Battery Aging in Electrified Vehicles

Huang

2019

Batteries

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Aging assessment is critical for lithium-ion batteries (LIBs) as the technology of choice for energy storage in electrified vehicles (EVs). Existing research is mainly focused on either increasing modeling precision or improving algorithm efficiency, while the significance of data applied for aging assessment has been largely overlooked. Moreover, reported studies are mostly confined to a specific condition without considering the impacts of diverse usage patterns on battery aging, which is practically challenging and can greatly affect battery degradation. This paper addresses these issues through incremental capacity (IC) analysis, which can both utilize data directly available from on-board sensors and interpret degradations from a physics-based perspective. Through IC analysis, the optimal health feature (HF) and the state of charge (SOC)-based optimal data profile for battery aging assessment have been identified. Four stress factors, i.e., depth-of-discharge (DOD), charging C-rate, operating mode, and temperature, have been selected to jointly characterize diverse usage patterns. Impact analysis of different stress factors through the optimal HF with the SOC-based optimal data profile from aging campaign experiments have generated practical guidance on usage patterns to improve battery health monitoring and lifetime control strategies.Batteries 2019, 5, 59 2 of 16 fitting. The simplicity and superiority in real-time computation have given rise to their wide applications in BMS [9]. Yet the lack of physical representation makes it unable to interpret battery degradation mechanisms and ultimately suffers from insufficient estimation accuracy. In contrast, the physics-based approach is built upon electrochemical models (EChMs) to gain insights into the internal physicochemical processes related with aging. However, the high modeling precision comes at the price of demanding computation resources, given that the governing equations of EChMs are mostly partial differential equations (PDEs) [11], which are not amendable for BMS application. Despite multiple model order reduction (MOR) techniques [14] to reduce the computational complexity with various introduced approximations, the balance between modeling precision and computation efficiency still faces severe challenges.The data-driven methods [17][18][19][20] have attracted tremendous attention in LIB aging research owing to their flexibilities from the model-free feature. The independence from battery physical properties makes it feasible for the application of various machine learning methods, including support vector machines (SVM) [17], artificial neural networks (ANN) [18], relevance vector machines (RVM) [19], and particle swarm optimization [20], etc., to develop mathematical descriptions of battery degradation behaviors through statistical learning from a large amount of data. Nevertheless, data-driven methods are often subject to certain drawbacks, such as data saturation and specific input requirement. And the reliability of data-driven methods stron...

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Section: Obtain the Incremental Capacity (Ic) Curvementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Incremental Capacity Analysis-Based Impact Study of Diverse Usage Patterns on Lithium-Ion Battery Aging in Electrified Vehicles

Huang

2019

Batteries

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Waste Prevention for Energy Storage Devices Based on Second‐Life Use of Lithium‐Ion Batteries

Pohl

Collis

Mahon

et al. 2023

Sustainable Energy Storage in the Scope of Circular Economy

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Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

et al. 2021

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performance and cost are not satisfactory while the energy density of conventional LIBs have almost reached the theoretical maximum. To further advance current LIBs, numerous efforts have been devoted to the exploration of new electrode and electrolyte materials. [3] The historical material research has heavily relied on either "trial-and-error" processes or serendipity, both of which require the vast numbers of tedious experiments (Figure 1a). Such intuition-based approaches are often time-consuming and inefficient, which cannot avoid the consumption of many manpower and material resources. In the past 50 years, computational chemistry, such as firstprinciples (FP) calculations, [4,5] quantum mechanics, [6] molecular dynamics (MD) [7] and Monte Carlo techniques, [8] has become a mature approach to complement and aid experimental studies for predicting and designing new materials. With the rapid development of high-performance computations, density functional theory (DFT) has been widely applied to high-throughput property prediction, which is conducive to the development of materials databases, such as Inorganic Crystal Structure Database (ICSD), [9] Cambridge Structural Database, [10] the Materials Project [11] database, AFLOWLIB consortium, [12] Open Quantum Materials Database, [13] Harvard Clean Energy Project, [14] Electronic Structure Project, [15] MaterialGo, [16] and so on. However, Lithium-ion batteries (LIBs) are vital energy-storage devices in modern society. However, the performance and cost are still not satisfactory in terms of energy density, power density, cycle life, safety, etc. To further improve the performance of batteries, traditional "trial-and-error" processes require a vast number of tedious experiments. Computational chemistry and artificial intelligence (AI) can significantly accelerate the research and development of novel battery systems. Herein, a heterogeneous category of AI technology for predicting and discovering battery materials and estimating the state of the battery system is reviewed. Successful examples, the challenges of deploying AI in real-world scenarios, and an integrated framework are analyzed and outlined. The state-of-the-art research about the applications of ML in the property prediction and battery discovery, including electrolyte and electrode materials, are further summarized. Meanwhile, the prediction of battery states is also provided. Finally, various existing challenges and the framework to tackle the challenges on the further development of machine learning for rechargeable LIBs are proposed.

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A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter

Cited by 371 publications

References 53 publications

Incremental Capacity Analysis-Based Impact Study of Diverse Usage Patterns on Lithium-Ion Battery Aging in Electrified Vehicles

Incremental Capacity Analysis-Based Impact Study of Diverse Usage Patterns on Lithium-Ion Battery Aging in Electrified Vehicles

Waste Prevention for Energy Storage Devices Based on Second‐Life Use of Lithium‐Ion Batteries

Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries

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