Aging of a Lithium-Metal/LFP Cell: Predictive Model and Experimental Validation
Actual market requirements for storage systems highlight the limits of graphite as an anode for Li-ion batteries. Lithium metal can represent a suitable alternative to graphite due to its high theoretical specific capacity (about 3860 mAh g<sup>−1</sup>) and low negative redox potential....
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-02-01
|
Series: | Batteries |
Subjects: | |
Online Access: | https://www.mdpi.com/2313-0105/9/3/146 |
_version_ | 1827751297180237824 |
---|---|
author | Davide Dessantis Piera Di Prima Daniele Versaci Julia Amici Carlotta Francia Silvia Bodoardo Massimo Santarelli |
author_facet | Davide Dessantis Piera Di Prima Daniele Versaci Julia Amici Carlotta Francia Silvia Bodoardo Massimo Santarelli |
author_sort | Davide Dessantis |
collection | DOAJ |
description | Actual market requirements for storage systems highlight the limits of graphite as an anode for Li-ion batteries. Lithium metal can represent a suitable alternative to graphite due to its high theoretical specific capacity (about 3860 mAh g<sup>−1</sup>) and low negative redox potential. However, several aging mechanisms, such as dendrite growth, lithium loss and the formation of an unstable SEI, decrease the performances of Li-based batteries. A suitable strategy to better understand and study these mechanisms could be the development of an electrochemical model that forecasts the aging behaviour of a lithium-metal battery. In this work, a P2D aging electrochemical model for an Li-based cell was developed. The main innovation is represented by the combination of two aspects: the substitution of graphite with metallic lithium as an anode and the implementation of SEI growth on the metallic lithium surface. The calibration of the model, based on experimental measurements and the successive validation, led to us obtaining a good accuracy between the simulated and experimental curves. This good accuracy makes the developed P2D aging model a versatile and suitable approach for further investigations on Li-based batteries considering all the aging phenomena involved. |
first_indexed | 2024-03-11T06:56:16Z |
format | Article |
id | doaj.art-2a381812ad744b989a5e7d8c04fa8bb9 |
institution | Directory Open Access Journal |
issn | 2313-0105 |
language | English |
last_indexed | 2024-03-11T06:56:16Z |
publishDate | 2023-02-01 |
publisher | MDPI AG |
record_format | Article |
series | Batteries |
spelling | doaj.art-2a381812ad744b989a5e7d8c04fa8bb92023-11-17T09:35:55ZengMDPI AGBatteries2313-01052023-02-019314610.3390/batteries9030146Aging of a Lithium-Metal/LFP Cell: Predictive Model and Experimental ValidationDavide Dessantis0Piera Di Prima1Daniele Versaci2Julia Amici3Carlotta Francia4Silvia Bodoardo5Massimo Santarelli6Department of Energy, Polytechnic of Turin, 10129 Turin, ItalyDepartment of Energy, Polytechnic of Turin, 10129 Turin, ItalyDepartment of Applied Science and Technology, Polytechnic of Turin, 10129 Turin, ItalyDepartment of Applied Science and Technology, Polytechnic of Turin, 10129 Turin, ItalyDepartment of Applied Science and Technology, Polytechnic of Turin, 10129 Turin, ItalyDepartment of Applied Science and Technology, Polytechnic of Turin, 10129 Turin, ItalyDepartment of Energy, Polytechnic of Turin, 10129 Turin, ItalyActual market requirements for storage systems highlight the limits of graphite as an anode for Li-ion batteries. Lithium metal can represent a suitable alternative to graphite due to its high theoretical specific capacity (about 3860 mAh g<sup>−1</sup>) and low negative redox potential. However, several aging mechanisms, such as dendrite growth, lithium loss and the formation of an unstable SEI, decrease the performances of Li-based batteries. A suitable strategy to better understand and study these mechanisms could be the development of an electrochemical model that forecasts the aging behaviour of a lithium-metal battery. In this work, a P2D aging electrochemical model for an Li-based cell was developed. The main innovation is represented by the combination of two aspects: the substitution of graphite with metallic lithium as an anode and the implementation of SEI growth on the metallic lithium surface. The calibration of the model, based on experimental measurements and the successive validation, led to us obtaining a good accuracy between the simulated and experimental curves. This good accuracy makes the developed P2D aging model a versatile and suitable approach for further investigations on Li-based batteries considering all the aging phenomena involved.https://www.mdpi.com/2313-0105/9/3/146lithium-based batterymetallic lithiumsolid electrolyte interphaseP2D modelingaging |
spellingShingle | Davide Dessantis Piera Di Prima Daniele Versaci Julia Amici Carlotta Francia Silvia Bodoardo Massimo Santarelli Aging of a Lithium-Metal/LFP Cell: Predictive Model and Experimental Validation Batteries lithium-based battery metallic lithium solid electrolyte interphase P2D modeling aging |
title | Aging of a Lithium-Metal/LFP Cell: Predictive Model and Experimental Validation |
title_full | Aging of a Lithium-Metal/LFP Cell: Predictive Model and Experimental Validation |
title_fullStr | Aging of a Lithium-Metal/LFP Cell: Predictive Model and Experimental Validation |
title_full_unstemmed | Aging of a Lithium-Metal/LFP Cell: Predictive Model and Experimental Validation |
title_short | Aging of a Lithium-Metal/LFP Cell: Predictive Model and Experimental Validation |
title_sort | aging of a lithium metal lfp cell predictive model and experimental validation |
topic | lithium-based battery metallic lithium solid electrolyte interphase P2D modeling aging |
url | https://www.mdpi.com/2313-0105/9/3/146 |
work_keys_str_mv | AT davidedessantis agingofalithiummetallfpcellpredictivemodelandexperimentalvalidation AT pieradiprima agingofalithiummetallfpcellpredictivemodelandexperimentalvalidation AT danieleversaci agingofalithiummetallfpcellpredictivemodelandexperimentalvalidation AT juliaamici agingofalithiummetallfpcellpredictivemodelandexperimentalvalidation AT carlottafrancia agingofalithiummetallfpcellpredictivemodelandexperimentalvalidation AT silviabodoardo agingofalithiummetallfpcellpredictivemodelandexperimentalvalidation AT massimosantarelli agingofalithiummetallfpcellpredictivemodelandexperimentalvalidation |