{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/40314"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/40314","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Francis, Christopher"],"institution":"Department of Electrical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Mwangama, Joyce","Awodele Kehinde"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-22T22:23:47Z","subjects":["Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/40314","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Mwangama, Joyce","Awodele Kehinde"]},{"key":"dc:creator","label":"Author","values":["Francis, Christopher"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-07-04T13:56:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-07-04T13:56:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electrical Engineering"]},{"key":"dc:type","label":"Dc Type","values":["Thesis / Dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/40314"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Mwangama, Joyce","Awodele Kehinde"],"dc:creator":["Francis, Christopher"],"dc:date.accessioned":["2024-07-04T13:56:15Z"],"dc:date.available":["2024-07-04T13:56:15Z"],"dc:date.issued":["2024"],"dc:description.abstract":["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."],"dc:identifier.uri":["http://hdl.handle.net/11427/40314"],"dc:publisher.department":["Department of Electrical Engineering"],"dc:subject":["Engineering"],"dc:title":["Comparative Analysis of Coulomb Counting and Extended Kalman Filter for State of Charge Estimation in Battery Management Systems"],"dc:type":["Thesis / Dissertation"],"dc:type.qualificationlevel":["Masters","MSc"]},"updated_at":"2026-07-22T22:23:47Z"}