Smart computing for thermal dynamics
Real-time computing and simulation of thermal dynamics of lithium-ion batteries, which are widely used in today’s technology is an important part in maintaining the optimal temperature range of the battery during operation. The finite-element method (FEM), while able to accurately solve and model sa...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/177026 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Real-time computing and simulation of thermal dynamics of lithium-ion batteries, which are widely used in today’s technology is an important part in maintaining the optimal temperature range of the battery during operation. The finite-element method (FEM), while able to accurately solve and model said thermal dynamics problem, is too slow to be implanted for real-time operations. Therefore, techniques to solve computing time problem by reduction or simplifying the thermal dynamics problem is needed. This paper explores the characteristics of such solutions, in particular, Proper Orthogonal Decomposition (POD), one of the known methods in model-order-reduction to reduce the computing power needed for the thermal model to the point that real-time thermal monitoring is viable yet accurate. The POD method is used to find the most excited modes that represent the main behaviour in the data by analysing snapshots from the dataset to create an optimal basis that will be used to reconstruct the solution. The POD method is compared against the FEM in terms of accuracy and computing time needed in MATLAB and the results indeed show that POD method computes the thermal model significantly faster than the FEM while only losing an insignificant amount of accuracy. |
|---|