Holistic battery management system design for lithium-ion battery systems via physics-based modeling, estimation, and control
Abstract/Contents
- Abstract
- Lithium-ion battery systems used in electric vehicles and stationary grid storage applications are composed of numerous batteries that are interconnected to create a battery pack that can satisfy the high energy and power requirements of the desired application. However, the current research in the battery modeling and control community has focused mainly on lithium-ion batteries at the single cell-level in an isolated environment where the cell-to-cell interconnections and pack heterogeneities are not accounted for. Merely applying the existing knowledge of a single cell to such a large-scale battery pack assumes "modularity", wherein modularity is defined as the ability to extrapolate the behavior of a battery pack from a single cell. Recent experimental studies presented in the literature show evidence that the assumption of modularity, in terms of electrical, thermal, and aging behavior, does not hold true. The literature further highlights that a pack reaches its end-of-life sooner than a single cell, the thermal and aging gradient behavior of the pack is non-uniform and aggravated in comparison to a single cell, and the performance of a pack is adversely affected due to cell-to-cell heterogeneities induced by manufacturing variances. As a result, the design of Battery Management Systems for a pack must take these non-uniformities or peculiarities into account while developing algorithms for modeling, estimation, and control. To that end, this dissertation adopts a bottom-up approach by developing modeling and estimation tools at the cell-level, and then extending it to the module/pack-level for efficient control. An experimentally validated electrochemical model at the single cell-level forms the basis to develop a model-based observer to estimate "non-measurable" internal battery health variables. The cell-level electrochemical model is extended to a high-fidelity module-level model by incorporating the thermal, electrical, and aging interactions between cells to analytically and quantitatively understand the effect of heterogeneities and gradients on the behavior of battery modules. Subsequently, the model is utilized to develop an optimization-based control strategy to minimize the non-uniformities, thereby improving the safety and lifespan of battery modules. The outcome of this research will open up opportunities to advance knowledge of cell- and module-level dynamics, accurate real-time prognostic algorithms, and health-conscious module-level control. This research is primarily targeted towards the transportation sector (electric vehicles), but it can be extended to stationary grid storage applications, and more importantly used to determine the feasibility of using end-of-life lithium-ion cells in "second-use" applications.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Allam, Anirudh |
|---|---|
| Degree supervisor | Onori, Simona |
| Thesis advisor | Onori, Simona |
| Thesis advisor | Durlofsky, Louis |
| Thesis advisor | Kovscek, Anthony R. (Anthony Robert) |
| Degree committee member | Durlofsky, Louis |
| Degree committee member | Kovscek, Anthony R. (Anthony Robert) |
| Associated with | Stanford University, Department of Energy Resources Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Anirudh Allam. |
|---|---|
| Note | Submitted to the Department of Energy Resources Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/rw478bk4517 |
Access conditions
- Copyright
- © 2021 by Anirudh Allam
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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