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Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems
State of Charge (SOC) is simply a measure of the amount of available charge in a battery cell. It is not possible to directly measure SOC because it is a function of the stoichiometric concentration of ions in the cell, hence current and voltage measurements were used to obtain the required accurate...
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| Format: | Thesis |
| Language: | Eng |
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Department of Electrical Engineering
2024
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| _version_ | 1867614424932548608 |
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| access_status_str | Open Access |
| author | Francis, Christopher |
| author2 | Mwangama, Joyce |
| author_browse | Francis, Christopher Mwangama, Joyce |
| author_facet | Mwangama, Joyce Francis, Christopher |
| author_sort | Francis, Christopher |
| collection | Thesis |
| description | State of Charge (SOC) is simply a measure of the amount of available charge in a battery cell. It is not possible to directly measure SOC because it is a function of the stoichiometric concentration of ions in the cell, hence current and voltage measurements were used to obtain the required accurate and precise estimation. Various authors have proposed methods for estimating SOC, however most authors have presented only high level reports. In this research, a comparative investigation of the traditional Coulomb Counting (CC) method, and the state-of-the-art Extended Kalman Filter method for SOC estimation was undertaken using a model based approach, involving simulation using Simulink and Simscape. Besides a current integration model, a cell model was developed and parameterized using a Lithium based Nickel Cobalt Aluminium (NCA) oxide battery's pulse discharge test data. The Extended Kalman Filter (EKF) was implemented to estimate the SOC of the cell model and the performance of the estimation models were evaluated on the metric of RMSE, and convergence time. It was concluded that the EKF method, outperformed the CC method as a state-of-the-art SOC estimation technique, employed in battery management system (BMS) by battery developers for the EV use case. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40314 |
| institution | University of Cape Town (South Africa) |
| language | Eng |
| last_indexed | 2026-06-10T12:51:50.008Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40314 Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems Francis, Christopher Mwangama, Joyce Awodele Kehinde Engineering State of Charge (SOC) is simply a measure of the amount of available charge in a battery cell. It is not possible to directly measure SOC because it is a function of the stoichiometric concentration of ions in the cell, hence current and voltage measurements were used to obtain the required accurate and precise estimation. Various authors have proposed methods for estimating SOC, however most authors have presented only high level reports. In this research, a comparative investigation of the traditional Coulomb Counting (CC) method, and the state-of-the-art Extended Kalman Filter method for SOC estimation was undertaken using a model based approach, involving simulation using Simulink and Simscape. Besides a current integration model, a cell model was developed and parameterized using a Lithium based Nickel Cobalt Aluminium (NCA) oxide battery's pulse discharge test data. The Extended Kalman Filter (EKF) was implemented to estimate the SOC of the cell model and the performance of the estimation models were evaluated on the metric of RMSE, and convergence time. It was concluded that the EKF method, outperformed the CC method as a state-of-the-art SOC estimation technique, employed in battery management system (BMS) by battery developers for the EV use case. 2024-07-04T13:56:15Z 2024-07-04T13:56:15Z 2024 2024-07-03T13:40:19Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40314 Eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment |
| spellingShingle | Engineering Francis, Christopher Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems |
| thesis_degree_str | Master's |
| title | Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems |
| title_full | Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems |
| title_fullStr | Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems |
| title_full_unstemmed | Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems |
| title_short | Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems |
| title_sort | comparative analysis of coulomb counting and extended kalman filter for state of charge estimation in battery management systems |
| topic | Engineering |
| url | http://hdl.handle.net/11427/40314 |
| work_keys_str_mv | AT francischristopher comparativeanalysisofcoulombcountingandextendedkalmanfilterforstateofchargeestimationinbatterymanagementsystems |