| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets
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| Presentation Title |
Data-driven Model Based Comparison of Public Datasets for Online State of Charge Estimation in Lithium-ion Batteries |
| Author(s) |
Meghana Sudarshan, Alexey Serov, Casey Jones, Vikas Tomar |
| On-Site Speaker (Planned) |
Meghana Sudarshan |
| Abstract Scope |
Lithium-ion(Li-ion) batteries are widely used in energy storage systems, electric vehicles, and portable electronics, considering their high energy density and low self-discharge qualities. Online estimation of the state of charge (SOC) using battery management systems in Li-ion batteries is crucial to determine battery capacity fade and remaining useful life accurately. Due to the extent of datasets available, determining an appropriate combination of datasets covering most variabilities for training models to predict SOC is essential. Data-driven based machine learning algorithms are used in this work to predict SOC by measuring battery operational parameters and material parameters due to their exceptional learning abilities and high accuracy. Machine learning models are trained on input-output pairs from various publicly available datasets of 1860 Li-ion batteries. These models are tested for accuracy and compared based on real-time data prediction on battery material parameters as well as operational parameters, including voltage, current, and temperature. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, |