| Title: | Identification of Lithium-Ion Battery Parameter Variations Across Cells using Artificial Intelligence |
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| Authors: | ID Lubej, Tine (Author)
ID Truntič, Mitja (Mentor) More about this mentor...  |
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| Files: |  MAG_Lubej_Tine_2025.pdf (5,99 MB)
MD5: 8C6ACE3A1AB1AA25A714CA882FC4B6D9
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| Language: | English |
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| Work type: | Master's thesis/paper |
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| Typology: | 2.09 - Master's Thesis |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | This thesis focuses on improving the simulation, estimation, and accuracy of parameter identification in lithium-ion battery models. The key objective was to enhance a previously developed program by transitioning it to an object-oriented design, making it more efficient, user-friendly, and modular. Additionally, efforts were made to optimize the parameter estimation process by upgrading the cost function used during simulations and integrating real-world battery measurement data, specifically for the LGM50 battery type.
The first step in the thesis involved reworking the codebase to an object-oriented structure, which improved not only the code’s clarity but also its extensibility and efficiency. With this change, the program was better suited for future improvements and became more accessible for other users through simplified installation procedures. This was accompanied by the implementation of unit testing to ensure the reliability of the code.
Experiments were conducted across a range of discharge rates (from 0.05C to 1C) to evaluate the performance of the model under different conditions. These tests helped to identify trends in how the model responded to changes in operational parameters. Additionally, a dynamic pulse test was performed, which allowed for more precise estimation of the parameters. The results of these tests demonstrated the robustness of the methodology, especially under dynamic conditions.
A major innovation introduced in this thesis was the development of a new cost function, which led to noticeable improvements in parameter estimation accuracy, particularly under high discharge rates and when estimating multiple parameters simultaneously. This new cost function proved especially effective in more complex scenarios, where the original cost function struggled to maintain the same level of accuracy.
The program’s capabilities were further extended by incorporating real experimental data. Using a constant discharge profile for the LGM50 battery, the results showed some challenges when dealing with real-world data, particularly due to issues in measurement or data preprocessing. Nonetheless, the model consistently produced solutions, although the accuracy was influenced by the quality of the input data.
The thesis concludes by highlighting the success of the improvements made, both in terms of the program’s structure and the precision of its estimations. However, it also emphasizes the importance of improving the quality of real-world data to fully leverage the model’s potential in practical applications. This work lays a foundation for future developments in battery modeling, providing a framework that is adaptable for further research and practical use. |
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| Keywords: | Machine Learning, Lithium-Ion Batteries, Parameter Estimation, Uncertainty Quantification, Real-experimental data |
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| Place of publishing: | Maribor |
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| Publisher: | [T. Lubej] |
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| Year of publishing: | 2024 |
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| PID: | 20.500.12556/DKUM-90850 |
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| UDC: | 681.5.015:621.354(043.2) |
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| COBISS.SI-ID: | 229492227 |
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| Publication date in DKUM: | 03.03.2025 |
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| Views: | 299 |
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| Downloads: | 58 |
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| Metadata: |    |
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| Categories: | KTFMB - FERI
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