Battery models can be developed from first principles or from empirical methods. The work presented in this thesis is semi-empirical, the model was validated using test data through parameter optimization. Simulink Parameter Estimation toolbox was used to identify the battery parameters and validate the battery model with test data. Experimental data was obtained by discharging the battery of a modified 2013 Chevrolet Malibu hybrid electric vehicle. The resulting battery model provided accurate simulation results over the validation data. For the constant current discharge, the mean squared error between measured and simulated data was 0.26 volts for the terminal voltage and 6.07e-4 (%) for state of charge. For the extended variable current discharge, the mean squared error between measured and simulated data was 0.21 volts for terminal voltage and 9.25e-4 (%) for state of charge. The validated battery model was implemented in the hybrid electric vehicle model and an optimization routine was conducted in Simulink to validate a launch control strategy. The vehicle model was subject to two maximum acceleration tests from 0-60mph. Test 1 corresponded to a maximum acceleration in EV-only mode and test 2 corresponded to a maximum acceleration in HEV mode or launch control mode. In both tests, the simulated data matched the experimental data with a root mean square error below 0.45 mph for vehicle speed and 3.5 volts for bus voltage.
","abstract_html":"<p>Battery models can be developed from first principles or from empirical methods. The work presented in this thesis is semi-empirical, the model was validated using test data through parameter optimization. Simulink Parameter Estimation toolbox was used to identify the battery parameters and validate the battery model with test data. Experimental data was obtained by discharging the battery of a modified 2013 Chevrolet Malibu hybrid electric vehicle. The resulting battery model provided accurate simulation results over the validation data. For the constant current discharge, the mean squared error between measured and simulated data was 0.26 volts for the terminal voltage and 6.07e-4 (%) for state of charge. For the extended variable current discharge, the mean squared error between measured and simulated data was 0.21 volts for terminal voltage and 9.25e-4 (%) for state of charge. The validated battery model was implemented in the hybrid electric vehicle model and an optimization routine was conducted in Simulink to validate a launch control strategy. The vehicle model was subject to two maximum acceleration tests from 0-60mph. Test 1 corresponded to a maximum acceleration in EV-only mode and test 2 corresponded to a maximum acceleration in HEV mode or launch control mode. In both tests, the simulated data matched the experimental data with a root mean square error below 0.45 mph for vehicle speed and 3.5 volts for bus voltage.</p>","abstract_has_math":false,"creators":["Khalifi, Hasnaa"],"institution":null,"degree_name":"Master of Science in Mechanical Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-03-01T08:00:00Z","date_published":"2015-03-01T08:00:00Z","updated_at":"2026-07-27T19:26:08Z","subjects":["hybrid electric","power","parameter optimization","Electrical and Computer Engineering","Power and Energy"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/220","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Khalifi, Hasnaa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mechanical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["hybrid electric","power","parameter optimization","Electrical and Computer Engineering","Power and Energy"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/220"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Battery models can be developed from first principles or from empirical methods. The work presented in this thesis is semi-empirical, the model was validated using test data through parameter optimization. Simulink Parameter Estimation toolbox was used to identify the battery parameters and validate the battery model with test data. Experimental data was obtained by discharging the battery of a modified 2013 Chevrolet Malibu hybrid electric vehicle. The resulting battery model provided accurate simulation results over the validation data. For the constant current discharge, the mean squared error between measured and simulated data was 0.26 volts for the terminal voltage and 6.07e-4 (%) for state of charge. For the extended variable current discharge, the mean squared error between measured and simulated data was 0.21 volts for terminal voltage and 9.25e-4 (%) for state of charge. The validated battery model was implemented in the hybrid electric vehicle model and an optimization routine was conducted in Simulink to validate a launch control strategy. The vehicle model was subject to two maximum acceleration tests from 0-60mph. Test 1 corresponded to a maximum acceleration in EV-only mode and test 2 corresponded to a maximum acceleration in HEV mode or launch control mode. In both tests, the simulated data matched the experimental data with a root mean square error below 0.45 mph for vehicle speed and 3.5 volts for bus voltage.
"]},{"key":"dc:title","label":"Title","values":["Hybrid Electric Power System Validation through Parameter Optimization"]}]}],"canonical_facts":{"dc:creator":["Khalifi, Hasnaa"],"dc:description.abstract":["Battery models can be developed from first principles or from empirical methods. The work presented in this thesis is semi-empirical, the model was validated using test data through parameter optimization. Simulink Parameter Estimation toolbox was used to identify the battery parameters and validate the battery model with test data. Experimental data was obtained by discharging the battery of a modified 2013 Chevrolet Malibu hybrid electric vehicle. The resulting battery model provided accurate simulation results over the validation data. For the constant current discharge, the mean squared error between measured and simulated data was 0.26 volts for the terminal voltage and 6.07e-4 (%) for state of charge. For the extended variable current discharge, the mean squared error between measured and simulated data was 0.21 volts for terminal voltage and 9.25e-4 (%) for state of charge. The validated battery model was implemented in the hybrid electric vehicle model and an optimization routine was conducted in Simulink to validate a launch control strategy. The vehicle model was subject to two maximum acceleration tests from 0-60mph. Test 1 corresponded to a maximum acceleration in EV-only mode and test 2 corresponded to a maximum acceleration in HEV mode or launch control mode. In both tests, the simulated data matched the experimental data with a root mean square error below 0.45 mph for vehicle speed and 3.5 volts for bus voltage.
"],"dc:identifier":["https://commons.erau.edu/edt/220"],"dc:subject":["hybrid electric","power","parameter optimization","Electrical and Computer Engineering","Power and Energy"],"dc:title":["Hybrid Electric Power System Validation through Parameter Optimization"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-27T19:26:08Z"}