Physics and machine learning based approaches to model energy storage systems
Abstract/Contents
- Abstract
- Energy storage systems ensure the effective and reliable management of power supply and demand. Despite the widespread adoption, energy storage systems face substantial challenges (e.g., safety concerns, limited capacity, environmental impact), stemming from their relatively brief history in academic research and industrial applications. Motivated by these challenges, this thesis explores and develops physics and machine learning based approaches to model energy storage systems, focusing on lithium ion batteries and fuel cells. We first scrutinize the impact of mechanical and thermal factors on lithium plating in lithium ion batteries, employing both 1D and 3D physics-based models. We find that mechanical deformation can accelerate the onset of lithium plating, with folds and boundaries of jellyrolls being particularly susceptible due to distinctive mechanical conditions. Next, motivated by the crucial role of mechanical deformation in lithium plating, we develop SenseNet, a physics-informed deep learning model, for shape sensing applications. SenseNet is crafted to monitor the mechanical deformation of arbitrary structures without relying on prior knowledge of loading conditions, but utilizing a discrete network of strain sensors. Finally, given the complex nature of multiphysics problems and the notable efficiency and accuracy of machine learning methods, we explore and compare diverse data-driven approaches for fuel cell modeling.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Qiu, Yitao |
|---|---|
| Degree supervisor | Linder, Christian, (Engineering professor) |
| Thesis advisor | Linder, Christian, (Engineering professor) |
| Thesis advisor | Borja, Ronaldo I. (Ronaldo Israel) |
| Thesis advisor | Sun, Waiching |
| Degree committee member | Borja, Ronaldo I. (Ronaldo Israel) |
| Degree committee member | Sun, Waiching |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yitao Qiu. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/zt068ns3636 |
Access conditions
- Copyright
- © 2023 by Yitao Qiu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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