Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network
Accurate evaluation of the health status of lithium-ion batteries must be deemed as of great significance, insofar as the utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal haza...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/9/7/350 |
| _version_ | 1797590308286889984 |
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| author | Miao Bai Chao Lyu Dazhi Yang Gareth Hinds |
| author_facet | Miao Bai Chao Lyu Dazhi Yang Gareth Hinds |
| author_sort | Miao Bai |
| collection | DOAJ |
| description | Accurate evaluation of the health status of lithium-ion batteries must be deemed as of great significance, insofar as the utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal hazards to, the health of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such as being fast and non-destructive over its competitors, suffices in detecting lithium plating and thus has been attracting increasing attention in the field of battery management, but its ability of assessing quantitatively the degree of lithium plating remains largely unexplored hitherto. On this point, this work seeks to narrow that gap by proposing an EIS-based method that can quantify the degree of lithium plating. The core conception is to eventually circumvent the reliance on state-of-health measurement, and use instead the impedance spectrum to acquire an estimate on battery capacity loss. To do so, the effects of solid electrolyte interphase formation and lithium plating on battery capacity must be first decoupled, so that the mass of lithium plating can be quantified. Then, based on an impedance spectrum measurement, the parameters of the fractional equivalent circuit model (ECM) of the battery can be identified. These fractional ECM parameters are received as inputs by an artificial neural network, which is tasked with establishing a correspondence between the model parameters and the mass of lithium plating. The empirical part of the work revolves around the data collected from an aging experiment, and the validity of the proposed method is truthfully attested by dismantling the batteries, which is otherwise not needed during the actual uptake of the method. |
| first_indexed | 2024-03-11T01:18:44Z |
| format | Article |
| id | doaj.art-8d403265f3124449810b8951eb6130f9 |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-11T01:18:44Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-8d403265f3124449810b8951eb6130f92023-11-18T18:18:44ZengMDPI AGBatteries2313-01052023-07-019735010.3390/batteries9070350Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural NetworkMiao Bai0Chao Lyu1Dazhi Yang2Gareth Hinds3School of Electical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaNational Physical Laboratory, Teddington, Middlesex TW11 0LW, UKAccurate evaluation of the health status of lithium-ion batteries must be deemed as of great significance, insofar as the utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal hazards to, the health of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such as being fast and non-destructive over its competitors, suffices in detecting lithium plating and thus has been attracting increasing attention in the field of battery management, but its ability of assessing quantitatively the degree of lithium plating remains largely unexplored hitherto. On this point, this work seeks to narrow that gap by proposing an EIS-based method that can quantify the degree of lithium plating. The core conception is to eventually circumvent the reliance on state-of-health measurement, and use instead the impedance spectrum to acquire an estimate on battery capacity loss. To do so, the effects of solid electrolyte interphase formation and lithium plating on battery capacity must be first decoupled, so that the mass of lithium plating can be quantified. Then, based on an impedance spectrum measurement, the parameters of the fractional equivalent circuit model (ECM) of the battery can be identified. These fractional ECM parameters are received as inputs by an artificial neural network, which is tasked with establishing a correspondence between the model parameters and the mass of lithium plating. The empirical part of the work revolves around the data collected from an aging experiment, and the validity of the proposed method is truthfully attested by dismantling the batteries, which is otherwise not needed during the actual uptake of the method.https://www.mdpi.com/2313-0105/9/7/350artificial neural networklithium plating quantificationequivalent circuit modelparameter identificationfeature parameter extraction |
| spellingShingle | Miao Bai Chao Lyu Dazhi Yang Gareth Hinds Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network Batteries artificial neural network lithium plating quantification equivalent circuit model parameter identification feature parameter extraction |
| title | Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network |
| title_full | Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network |
| title_fullStr | Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network |
| title_full_unstemmed | Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network |
| title_short | Quantification of Lithium Plating in Lithium-Ion Batteries Based on Impedance Spectrum and Artificial Neural Network |
| title_sort | quantification of lithium plating in lithium ion batteries based on impedance spectrum and artificial neural network |
| topic | artificial neural network lithium plating quantification equivalent circuit model parameter identification feature parameter extraction |
| url | https://www.mdpi.com/2313-0105/9/7/350 |
| work_keys_str_mv | AT miaobai quantificationoflithiumplatinginlithiumionbatteriesbasedonimpedancespectrumandartificialneuralnetwork AT chaolyu quantificationoflithiumplatinginlithiumionbatteriesbasedonimpedancespectrumandartificialneuralnetwork AT dazhiyang quantificationoflithiumplatinginlithiumionbatteriesbasedonimpedancespectrumandartificialneuralnetwork AT garethhinds quantificationoflithiumplatinginlithiumionbatteriesbasedonimpedancespectrumandartificialneuralnetwork |