Feature-prompting Protocols for Data-driven Health Estimation and Lifetime Prediction of Lithium Metal Batteries
Abstract/Contents
- Abstract
- Lithium metal batteries (LMB) represent one of the most promising battery technologies due to their high energy density. However, the commercial adoption of LMBs has been hindered by their limited lifespan and safety concerns resulting from lithium metal’s high electrochemical reactivity. Effective health estimation and lifetime prediction could enable timely maintenance actions before failure and a rapid LMB development iteration. In this work, we first propose novel diagnostic test and aging cycling protocols that enable the tracking of LMB degradation from voltage-current measurements through extracting internal resistance, relaxation voltage, and charging impedance. With these features, we present an automatic data-driven aging modeling framework for LMBs. Validation over test cells demonstrate accurate health estimation performance using only one cycle of voltage and current data, and reliable lifetime prediction using cells’ early-life data. The proposed model provides explanatory insights tailored to LMBs, and does not require additional internal state sensors.
Description
| Type of resource | text |
|---|---|
| Publication date | December 18, 2023 |
Creators/Contributors
| Author | Ma, Wenting |
|---|---|
| Advisor | Onori, Simona |
Subjects
| Subject | Li-metal battery |
|---|---|
| Subject | health estimation |
| Subject | lifetime prediction |
| Subject | Machine learning |
| Genre | Text |
| Genre | Thesis |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International license (CC BY-NC).
Preferred citation
- Preferred citation
- Ma, W. (2023). Feature-prompting Protocols for Data-driven Health Estimation and Lifetime Prediction of Lithium Metal Batteries. Stanford Digital Repository. Available at https://purl.stanford.edu/zg095vn6290. https://doi.org/10.25740/zg095vn6290.
Collection
Master's Theses, Doerr School of Sustainability
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