| Title: | A genetic algorithm based ESC model to handle the unknown initial conditions of state of charge for lithium ion battery cell |
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| Authors: | ID Korez, Kristijan (Author)
ID Fister, Dušan (Author)
ID Šafarič, Riko (Author) |
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| Files: |  batteries-11-00001.pdf (5,96 MB)
MD5: D26421DA114C58D742B43B54DDD16373
 https://www.mdpi.com/2313-0105/11/1/1
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | Classic enhanced self-correcting battery equivalent models require proper model parameters and initial conditions such as the initial state of charge for its unbiased functioning. Obtaining parameters is often conducted by optimization using evolutionary algorithms. Obtaining the initial state of charge is often conducted by measurements, which can be burdensome in practice. Incorrect initial conditions can introduce bias, leading to long-term drift and inaccurate state of charge readings. To address this, we propose two simple and efficient equivalent model frameworks that are optimized by a genetic algorithm and are able to determine the initial conditions autonomously. The first framework applies the feedback loop mechanism that gradually with time corrects the externally given initial condition that is originally a biased arbitrary value within a certain domain. The second framework applies the genetic algorithm to search for an unbiased estimate of the initial condition. Long-term experiments have demonstrated that these frameworks do not deviate from controlled benchmarks with known initial conditions. Additionally, our experiments have shown that all implemented models significantly outperformed the well-known ampere-hour coulomb counter integration method, which is prone to drift over time and the extended Kalman filter, that acted with bias. |
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| Keywords: | enhanced self-correcting model, state of charge estimation, lithium-ion cell parameter identification |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 07.10.2024 |
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| Article acceptance date: | 18.12.2024 |
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| Publication date: | 24.12.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2025 |
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| Number of pages: | 26 str. |
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| Numbering: | Vol. 11, iss. 1 |
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| PID: | 20.500.12556/DKUM-91502  |
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| UDC: | 681.5 |
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| ISSN on article: | 2313-0105 |
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| COBISS.SI-ID: | 221279747 |
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| DOI: | 10.3390/batteries11010001 |
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| Copyright: | © 2024 by the authors |
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| Publication date in DKUM: | 08.01.2025 |
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| Views: | 231 |
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| Downloads: | 18 |
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| Metadata: |    |
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| Categories: | Misc.
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