Reduced-order modeling and adaptive observer design for lithium-ion battery cells
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2017
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/111722 |
| _version_ | 1826214294761504768 |
|---|---|
| author | Limoge, Damas Wilks |
| author2 | Anuradha M. Annaswamy. |
| author_facet | Anuradha M. Annaswamy. Limoge, Damas Wilks |
| author_sort | Limoge, Damas Wilks |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017. |
| first_indexed | 2024-09-23T15:15:35Z |
| format | Thesis |
| id | mit-1721.1/111722 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:15:35Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1117222019-04-12T22:53:26Z Reduced-order modeling and adaptive observer design for lithium-ion battery cells Limoge, Damas Wilks Anuradha M. Annaswamy. Massachusetts Institute of Technology. Department of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering. Mechanical Engineering. Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 167-171). This thesis discusses the design of a control-oriented modeling approach to Lithium- Ion battery modeling, as well as the application of adaptive observers to this structure. It begins by describing the fundamental problem statement of a battery management system (BMS), and why this is challenging to solve. It continues by describing, in brief, several different modeling techniques and their use cases, then fully expounds two separate high fidelity models. The first model, the ANCF, was initiated in previous work, and has been updated with novel features, such as dynamic diffusion coefficients. The second model, the ANCF II, was developed for this thesis and updates the previous model to better solve the problems facing the construction of an adaptive observer, while maintaining its model accuracy. The results of these models are presented as well. After establishing a model with the desired accuracy and complexity, foundational observers are designed to estimate the states and parameters of the time-varying ionic concentrations in the solid electrode and electrolyte, as well as an a-priori estimate of the molar flux. For the solid electrode, it is shown that a regressor matrix can be constructed for the observer using both spatial and temporal filters, limiting the amount of additional computation required for this purpose. For the molar flux estimate, it is shown that fast convergence is possible with coefficients pertaining to measurable inputs and outputs, and filters thereof. Finally, for the electrolyte observer, a novel structure is established to restrict learning only along unknown degrees of freedom of the model system, using a Jacobian steepest descent approach. Following the results of these observers, an outline is sketched for the application of a machine learning algorithm to estimate the nonlinear effects of cell dynamics. by Damas Wilks Limoge. S.M. 2017-10-04T15:05:29Z 2017-10-04T15:05:29Z 2017 2017 Thesis http://hdl.handle.net/1721.1/111722 1004236458 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 171 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Limoge, Damas Wilks Reduced-order modeling and adaptive observer design for lithium-ion battery cells |
| title | Reduced-order modeling and adaptive observer design for lithium-ion battery cells |
| title_full | Reduced-order modeling and adaptive observer design for lithium-ion battery cells |
| title_fullStr | Reduced-order modeling and adaptive observer design for lithium-ion battery cells |
| title_full_unstemmed | Reduced-order modeling and adaptive observer design for lithium-ion battery cells |
| title_short | Reduced-order modeling and adaptive observer design for lithium-ion battery cells |
| title_sort | reduced order modeling and adaptive observer design for lithium ion battery cells |
| topic | Mechanical Engineering. |
| url | http://hdl.handle.net/1721.1/111722 |
| work_keys_str_mv | AT limogedamaswilks reducedordermodelingandadaptiveobserverdesignforlithiumionbatterycells |