Lithium-ion battery calendar aging mechanism analysis and impedance-based State-of-Health estimation method. (1st August 2023)
- Record Type:
- Journal Article
- Title:
- Lithium-ion battery calendar aging mechanism analysis and impedance-based State-of-Health estimation method. (1st August 2023)
- Main Title:
- Lithium-ion battery calendar aging mechanism analysis and impedance-based State-of-Health estimation method
- Authors:
- Zhang, Qi
Wang, Dafang
Schaltz, Erik
Stroe, Daniel-Ioan
Gismero, Alejandro
Yang, Bowen - Abstract:
- Abstract: Calendar aging is an important part of lithium-ion battery aging research. In response to the problem that the aging history of a battery cell, whose State-of-Health (SOH) needs to be estimated, may be not available, this paper proposes a SOH estimation model not relying on calendric aging conditions such as storage State-of-Charge (SOC) and storage temperature. The aging mechanisms of lithium-ion batteries in different calendric aging conditions are analyzed to investigate the influences of different aging conditions on battery internal behaviors. The neural network is used to build the SOH estimation model. To prove that the model accuracy is not affected by battery aging history, SOH indicators of cells aged at different conditions are set as training data set and testing data set respectively, and trained SOH estimation accuracy and tested SOH estimation accuracy are compared. The comparison shows that increments of mean absolute error (MAE) of SOH estimation introduced by the aging condition difference between trained data and tested data are less than 2 %. Using SOH indicators obtained at different SOC levels as inputs of the model also hardly reduce the model accuracy. The increase of MAE of SOH estimation because of the SOC difference between trained data and tested data are less than 1.5 %. Highlights: The calendric SOH estimation model does not rely on inputs of aging conditions. Aging mechanisms of calendar aging at different conditions are compared.Abstract: Calendar aging is an important part of lithium-ion battery aging research. In response to the problem that the aging history of a battery cell, whose State-of-Health (SOH) needs to be estimated, may be not available, this paper proposes a SOH estimation model not relying on calendric aging conditions such as storage State-of-Charge (SOC) and storage temperature. The aging mechanisms of lithium-ion batteries in different calendric aging conditions are analyzed to investigate the influences of different aging conditions on battery internal behaviors. The neural network is used to build the SOH estimation model. To prove that the model accuracy is not affected by battery aging history, SOH indicators of cells aged at different conditions are set as training data set and testing data set respectively, and trained SOH estimation accuracy and tested SOH estimation accuracy are compared. The comparison shows that increments of mean absolute error (MAE) of SOH estimation introduced by the aging condition difference between trained data and tested data are less than 2 %. Using SOH indicators obtained at different SOC levels as inputs of the model also hardly reduce the model accuracy. The increase of MAE of SOH estimation because of the SOC difference between trained data and tested data are less than 1.5 %. Highlights: The calendric SOH estimation model does not rely on inputs of aging conditions. Aging mechanisms of calendar aging at different conditions are compared. Parameters whose decay trajectories do not rely on aging conditions are summarized. SOC changes in EIS measurements show little impact on accuracy of SOH estimation. … (more)
- Is Part Of:
- Journal of energy storage. Volume 64(2023)
- Journal:
- Journal of energy storage
- Issue:
- Volume 64(2023)
- Issue Display:
- Volume 64, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 64
- Issue:
- 2023
- Issue Sort Value:
- 2023-0064-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-08-01
- Subjects:
- Lithium-ion battery -- Calendar aging -- Aging mechanism -- State-of-Health estimation
Energy storage -- Periodicals
Energy storage -- Research -- Periodicals
621.3126 - Journal URLs:
- http://www.sciencedirect.com/science/journal/2352152X ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.est.2023.107029 ↗
- Languages:
- English
- ISSNs:
- 2352-152X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26931.xml