Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications
Abstract Lithium‐ion battery is the commonly used energy storage technology in electric vehicles (EVs) because of its inexpensive manufacturing cost and high energy capacity. For optimal utilization of its capacity and lifetime, reliable state of health (SoH) monitoring solutions have to be included...
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| Format: | Article |
| Language: | English |
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Wiley
2021-04-01
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| Series: | IET Smart Grid |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/stg2.12018 |
| _version_ | 1811179557917556736 |
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| author | Mehrnaz Javadipour Kamyar Mehran |
| author_facet | Mehrnaz Javadipour Kamyar Mehran |
| author_sort | Mehrnaz Javadipour |
| collection | DOAJ |
| description | Abstract Lithium‐ion battery is the commonly used energy storage technology in electric vehicles (EVs) because of its inexpensive manufacturing cost and high energy capacity. For optimal utilization of its capacity and lifetime, reliable state of health (SoH) monitoring solutions have to be included in the battery management system (BMS). SoH of a cell is affected by several reasons such as internal degradation or external damages that need to be estimated. This article analyses the current density in electrode and electrolyte of an EV lithium‐ion cell using a simulation assisted method that leads to improvement in SoH estimation accuracy. The experimental results are analysed through the fusion of the magnetic field images captured by quantum fluxgate magnetometers, installed on the surface of the cell, together with the real‐time simulation of the multi‐physics model of the cell. The magnetic field sensors measure the magnetic field intensity with an accuracy of ±2 mT. The real‐time simulation input data is updated from the measurements of both the magnetic field sensors and the battery cycler. The multi‐physics model of the cell is developed in COMSOL modelling software, and real‐time data fusion process is implemented on dSPACE Microlabbox real‐time simulator. Results confirm that the proposed monitoring solution provides useful insight that can be employed in ageing estimation of EV batteries. |
| first_indexed | 2024-04-11T06:37:12Z |
| format | Article |
| id | doaj.art-8de44a18a86c4c29b4a9ed5e218d8e80 |
| institution | Directory Open Access Journal |
| issn | 2515-2947 |
| language | English |
| last_indexed | 2024-04-11T06:37:12Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj.art-8de44a18a86c4c29b4a9ed5e218d8e802022-12-22T04:39:40ZengWileyIET Smart Grid2515-29472021-04-014217618910.1049/stg2.12018Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applicationsMehrnaz Javadipour0Kamyar Mehran1School of Electronic Engineering and Computer Science Queen Mary University of London Mile End Road LondonE1 4NS UKSchool of Electronic Engineering and Computer Science Queen Mary University of London Mile End Road LondonE1 4NS UKAbstract Lithium‐ion battery is the commonly used energy storage technology in electric vehicles (EVs) because of its inexpensive manufacturing cost and high energy capacity. For optimal utilization of its capacity and lifetime, reliable state of health (SoH) monitoring solutions have to be included in the battery management system (BMS). SoH of a cell is affected by several reasons such as internal degradation or external damages that need to be estimated. This article analyses the current density in electrode and electrolyte of an EV lithium‐ion cell using a simulation assisted method that leads to improvement in SoH estimation accuracy. The experimental results are analysed through the fusion of the magnetic field images captured by quantum fluxgate magnetometers, installed on the surface of the cell, together with the real‐time simulation of the multi‐physics model of the cell. The magnetic field sensors measure the magnetic field intensity with an accuracy of ±2 mT. The real‐time simulation input data is updated from the measurements of both the magnetic field sensors and the battery cycler. The multi‐physics model of the cell is developed in COMSOL modelling software, and real‐time data fusion process is implemented on dSPACE Microlabbox real‐time simulator. Results confirm that the proposed monitoring solution provides useful insight that can be employed in ageing estimation of EV batteries.https://doi.org/10.1049/stg2.12018ageingbattery management systemsbattery powered vehiclescurrent densityelectrochemical electrodeselectrolytes |
| spellingShingle | Mehrnaz Javadipour Kamyar Mehran Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications IET Smart Grid ageing battery management systems battery powered vehicles current density electrochemical electrodes electrolytes |
| title | Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications |
| title_full | Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications |
| title_fullStr | Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications |
| title_full_unstemmed | Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications |
| title_short | Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications |
| title_sort | analysis of current density in the electrode and electrolyte of lithium ion cells for ageing estimation applications |
| topic | ageing battery management systems battery powered vehicles current density electrochemical electrodes electrolytes |
| url | https://doi.org/10.1049/stg2.12018 |
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