A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell
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 ch...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2313-0105/11/1/1 |
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| author | Kristijan Korez Dušan Fister Riko Šafarič |
| author_facet | Kristijan Korez Dušan Fister Riko Šafarič |
| author_sort | Kristijan Korez |
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| description | 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. |
| format | Article |
| id | doaj-art-fa9be3c779fa48ad99993e65189fc786 |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-fa9be3c779fa48ad99993e65189fc7862025-01-24T13:22:21ZengMDPI AGBatteries2313-01052024-12-01111110.3390/batteries11010001A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery CellKristijan Korez0Dušan Fister1Riko Šafarič2Piktronik d.o.o., Cesta k Tamu 17, 2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, SloveniaFaculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, SloveniaClassic 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.https://www.mdpi.com/2313-0105/11/1/1enhanced self-correcting modelstate of charge estimationlithium-ion cell parameter identification |
| spellingShingle | Kristijan Korez Dušan Fister Riko Šafarič A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell Batteries enhanced self-correcting model state of charge estimation lithium-ion cell parameter identification |
| title | A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell |
| title_full | A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell |
| title_fullStr | A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell |
| title_full_unstemmed | A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell |
| title_short | A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell |
| title_sort | genetic algorithm based esc model to handle the unknown initial conditions of state of charge for lithium ion battery cell |
| topic | enhanced self-correcting model state of charge estimation lithium-ion cell parameter identification |
| url | https://www.mdpi.com/2313-0105/11/1/1 |
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