Battery degradation diagnosis with field data, impedance-based modeling and artificial intelligence

Li, Weihan; Chen, Jue; Quade, Katharina Lillith; Luder, Daniel; Gong, Jing‐Yu; Sauer, Dirk Uwe

doi:10.1016/j.ensm.2022.08.021

Cited by 47 publications

(15 citation statements)

References 45 publications

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“…Li et al integrated impedance data and charging data as basic data and conducted a cuckoo search algorithm to build up an online degradation diagnosis framework. 181 By collecting the field data, the model can accurately identify eight parameters, where five parameters are impedance-related parameters and the others are the OCV-related parameters, including remaining capacity and stoichiometric parameters at the end of charge and end of discharge. The identified model parameters could In summary, impedance-based models derived from ECM parameters and single-frequency impedance show better estimation performance, as the artificial selected features could reflect the battery states well.…”

Section: Machine Learning For Battery States Estimationmentioning

confidence: 99%

“…Besides only using impedance as inputs in the data-driven ML model, combining EIS with voltage profile and relevant differential curves may further improve the model accuracy. Li et al integrated impedance data and charging data as basic data and conducted a cuckoo search algorithm to build up an online degradation diagnosis framework . By collecting the field data, the model can accurately identify eight parameters, where five parameters are impedance-related parameters and the others are the OCV-related parameters, including remaining capacity and stoichiometric parameters at the end of charge and end of discharge.…”

Section: Application Of Eis To Lib’s Aging Studymentioning

confidence: 99%

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Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

Peng

Wei³

et al. 2023

J. Phys. Chem. C

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An in-depth understanding of battery degradation and aging in-Operando not only plays a vital role in the design of battery managing systems but also helps to ensure safe use and manufacturing optimization of lithium-ion batteries (LIBs) in large-scale applications. Electrochemical impedance spectroscopy (EIS) is a nondestructive method which unravels electrode kinetic processes inside the batteries in different time domains, including charge-transfer reactions, interfacial evolutions, and mass diffusions. It has become a powerful diagnosis and pre/prognosis tool in battery aging research, as it provides important insight into the changes of internal electrochemical processes by correlating the impedance evolution to degradation mechanisms. This review gives a critical overview on rapidly developing impedance techniques for degradation and aging investigation of Li-ion batteries. The EIS variations of LIBs at different aging conditions of calendar aging and accelerated aging are systematically summarized. In addition, the working principles, data validation, and modeling methods, including equivalent circuit model (ECM), distribution of relaxation times (DRT), and transmission line model (TLM), of classical EIS and dynamic EIS are elaborately concluded. Finally, the challenges and perspectives of further application of EIS in the aging research of LIBs are presented.

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Section: Machine Learning For Battery States Estimationmentioning

confidence: 99%

Section: Application Of Eis To Lib’s Aging Studymentioning

confidence: 99%

Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

Peng

Wei³

et al. 2023

J. Phys. Chem. C

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“…Basically, approaches by IC 46 and DV analysis 115 are employed for identifying the degradation mode of LIBs. Accordingly, researchers have attempted to resort to the customized aging data 28 or recognizable parameters 107 from battery models by describing intangible features or targets. Herein, the electrode‐level health diagnosis on LIBs was first studied by using the neural network (NN) model to identify the percentage degree of three degradation modes, trained by electrochemical features and targets, which were extracted from IC/DV curves and the manipulated aging modes from battery open circuit voltage (OCV) model, respectively 118 .…”

Section: Tasks In Battery Healthmentioning

confidence: 99%

“…In comparison, Duan et al 26 have built a CNN model to predict the whole impedance spectra of LIBs by feeding the raw CC charging data as the input feature. Li et al 107 have turned to resort to battery impedance parameters from the mechanistic model as targets, which simultaneously identifies the power and capacity fade.…”

Section: Tasks In Battery Healthmentioning

confidence: 99%

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Huang

Sugiarto

2023

EcoMat

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Lithium‐ion batteries (LIBs) have been dominating the markets of electric vehicles and grid energy storage. Accurate monitoring of battery health status has been one of the most critical challenges of the battery industry. Machine learning (ML) has been widely applied to battery health estimation as well as prediction. Here, by investigating the specific features and targets, we comprehensively discuss task‐oriented ML implementation in various application scenarios in the field of battery health. This review explores the tasks assisted by ML based on multi‐level cell degradation. We highlight opportunities and significance of considering the potential feature–target pair during the ML model training to identify more health information about LIBs as well as shed light into designing tasks for new application scenarios.

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“…8 In addition to the SOH value, the importance of estimating electrode-specific state of health (eSOH) parameters has been mentioned in many works. [9][10][11][12][13] The errors that can occur in the estimation of SOC and internal variables, if these parameters of eSOH are not accurately estimated, were highlighted in Part 1 of this series. 14 Besides, electrode-level health estimation could be useful for battery prognostics and degradation "knee" detection.…”

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confidence: 99%

A New Battery SOC/SOH/eSOH Estimation Method Using a PBM and Interconnected SPKFs: Part II. SOH and eSOH Estimation

Lopetegi,

Plett,

Trimboli

et al. 2024

J. Electrochem. Soc.

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Battery management systems (BMSs) are required to estimate many non-measurable values that describe the actual operating condition of batteries; such as state of charge (SOC) or state of health (SOH). In order to improve accuracy, many physical states and parameters can be estimated using physics-based models (PBMs). These estimates could be used to improve the control andprognosis of batteries. In a series of papers, we propose a new method to estimate internal physical states, SOC, SOH and other electrode-specific state of health (eSOH) parameters of a lithium-ion battery, using interconnected sigma-point Kalman filters (SPKFs) and a single-particle model with electrolyte dynamics (SPMe). This second paper focuses on eSOH parameter estimation. Simulationresults show that the method is capable of estimating the eSOH parameters and key degradation modes that can occur inside a lithium-ion battery cell using only cell voltage and current measurements.

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Battery degradation diagnosis with field data, impedance-based modeling and artificial intelligence

Cited by 47 publications

References 45 publications

Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

Application of Electrochemical Impedance Spectroscopy to Degradation and Aging Research of Lithium-Ion Batteries

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

A New Battery SOC/SOH/eSOH Estimation Method Using a PBM and Interconnected SPKFs: Part II. SOH and eSOH Estimation

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