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Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data - Part B : cycling operation
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Lucu, M., Martinez-Laserna, E., Gandiaga, I., Liu, Kailong, Camblong, H., Widanage, Widanalage Dhammika and Marco, James (2020) Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data - Part B : cycling operation. Journal of Energy Storage, 30 . 101410. doi:10.1016/j.est.2020.101410
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WRAP-data-driven-nonparametric-li-ion-battery-ageing-model-part-b-Marco-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2393Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.est.2020.101410
Abstract
Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing.
In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model.
The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||||||
| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Lithium ion batteries, Lithium ion batteries -- Storage, Lithium ion batteries -- Analysis | ||||||||||||
| Journal or Publication Title: | Journal of Energy Storage | ||||||||||||
| Publisher: | Elsevier | ||||||||||||
| ISSN: | 2352-152X | ||||||||||||
| Official Date: | August 2020 | ||||||||||||
| Dates: |
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| Date of first compliant deposit: | 19 May 2020 | ||||||||||||
| Volume: | 30 | ||||||||||||
| Article Number: | 101410 | ||||||||||||
| DOI: | 10.1016/j.est.2020.101410 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||
| RIOXX Funder/Project Grant: |
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