Dataset for rapid state of health estimation of lithium batteries using EIS and machine learning: Training and validation
This article addresses the objective, experimental design and methodology of the tests conducted for battery State of Health (SOH) estimation using an accelerated test method. For this purpose, 25 unused cylindrical cells were aged, by continual electrical cycling using a 0.5C charge and 1C discharg...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923002767 |
| _version_ | 1797798105728417792 |
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| author | Muhammad Rashid Mona Faraji-Niri Jonathan Sansom Muhammad Sheikh Dhammika Widanage James Marco |
| author_facet | Muhammad Rashid Mona Faraji-Niri Jonathan Sansom Muhammad Sheikh Dhammika Widanage James Marco |
| author_sort | Muhammad Rashid |
| collection | DOAJ |
| description | This article addresses the objective, experimental design and methodology of the tests conducted for battery State of Health (SOH) estimation using an accelerated test method. For this purpose, 25 unused cylindrical cells were aged, by continual electrical cycling using a 0.5C charge and 1C discharge to 5 different SOH breakpoints (80, 85, 90, 95 and 100%). Ageing of the cells to the different SOH values was undertaken at a temperature of 25 °C. A reference performance test (RPT) of C/3 charge-discharge at 25 °C was performed when the cells were new and at each stage of cycling to define the energy capacity reduction due to increased charge-throughput. An electrochemical impedance spectroscopy (EIS) test was performed at 5, 20, 50, 70 and 95% states of charge (SOC) for each cell at temperatures of 15, 25 and 35 °C. The shared data includes the raw data files for the reference test and the measured energy capacity and the measured SOH for each cell. It contains the 360 EIS data files and a file which tabulates the key features of the EIS plot for each test case. The reported data has been used to train a machine-learning model for the rapid estimation of battery SOH discussed in the manuscript co-submitted (MF Niri et al., 2022). The reported data can be used for the creation and validation of battery performance and ageing models to underpin different application studies and the design of control algorithms to be employed in battery management systems (BMS). |
| first_indexed | 2024-03-13T03:58:31Z |
| format | Article |
| id | doaj.art-e4146adb33244092b70fad32d325aba4 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-13T03:58:31Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-e4146adb33244092b70fad32d325aba42023-06-22T05:03:47ZengElsevierData in Brief2352-34092023-06-0148109157Dataset for rapid state of health estimation of lithium batteries using EIS and machine learning: Training and validationMuhammad Rashid0Mona Faraji-Niri1Jonathan Sansom2Muhammad Sheikh3Dhammika Widanage4James Marco5Corresponding author.; WMG, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UKWMG, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UKWMG, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UKWMG, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UKWMG, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UKWMG, University of Warwick, Gibbet Hill Road, Coventry, CV4 7AL, UKThis article addresses the objective, experimental design and methodology of the tests conducted for battery State of Health (SOH) estimation using an accelerated test method. For this purpose, 25 unused cylindrical cells were aged, by continual electrical cycling using a 0.5C charge and 1C discharge to 5 different SOH breakpoints (80, 85, 90, 95 and 100%). Ageing of the cells to the different SOH values was undertaken at a temperature of 25 °C. A reference performance test (RPT) of C/3 charge-discharge at 25 °C was performed when the cells were new and at each stage of cycling to define the energy capacity reduction due to increased charge-throughput. An electrochemical impedance spectroscopy (EIS) test was performed at 5, 20, 50, 70 and 95% states of charge (SOC) for each cell at temperatures of 15, 25 and 35 °C. The shared data includes the raw data files for the reference test and the measured energy capacity and the measured SOH for each cell. It contains the 360 EIS data files and a file which tabulates the key features of the EIS plot for each test case. The reported data has been used to train a machine-learning model for the rapid estimation of battery SOH discussed in the manuscript co-submitted (MF Niri et al., 2022). The reported data can be used for the creation and validation of battery performance and ageing models to underpin different application studies and the design of control algorithms to be employed in battery management systems (BMS).http://www.sciencedirect.com/science/article/pii/S2352340923002767Retired batteries2nd life applicationsState of health estimationBattery grading |
| spellingShingle | Muhammad Rashid Mona Faraji-Niri Jonathan Sansom Muhammad Sheikh Dhammika Widanage James Marco Dataset for rapid state of health estimation of lithium batteries using EIS and machine learning: Training and validation Data in Brief Retired batteries 2nd life applications State of health estimation Battery grading |
| title | Dataset for rapid state of health estimation of lithium batteries using EIS and machine learning: Training and validation |
| title_full | Dataset for rapid state of health estimation of lithium batteries using EIS and machine learning: Training and validation |
| title_fullStr | Dataset for rapid state of health estimation of lithium batteries using EIS and machine learning: Training and validation |
| title_full_unstemmed | Dataset for rapid state of health estimation of lithium batteries using EIS and machine learning: Training and validation |
| title_short | Dataset for rapid state of health estimation of lithium batteries using EIS and machine learning: Training and validation |
| title_sort | dataset for rapid state of health estimation of lithium batteries using eis and machine learning training and validation |
| topic | Retired batteries 2nd life applications State of health estimation Battery grading |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923002767 |
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