Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable Transportation
A Master of Science thesis in Electrical Engineering by Shoaib Ahmed entitled, “Optimizing the Performance of a Microwave Tomography System for Biomedical Applications”, submitted in April 2025. Thesis advisor is Dr. Habib ur Rehman and thesis co-advisors are Dr. Usman Tariq and Dr. Ammar Hasan. Sof...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | doctoralThesis |
| منشور في: |
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/26145 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513440672907264 |
|---|---|
| author | Ahmed, Shoaib |
| author_facet | Ahmed, Shoaib |
| author_role | author |
| dc.contributor.none.fl_str_mv | Rehman, Habib-ur Tariq, Usman Hasan, Ammar |
| dc.creator.none.fl_str_mv | Ahmed, Shoaib |
| dc.date.none.fl_str_mv | 2025-06-24T09:40:33Z 2025-06-24T09:40:33Z 2025-04 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2025.07 https://hdl.handle.net/11073/26145 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | Electric vehicles Lithium-ion batteries Induction motor Field-oriented control Reinforcement learning Fuzzy logic controller |
| dc.title.none.fl_str_mv | Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable Transportation |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Electrical Engineering by Shoaib Ahmed entitled, “Optimizing the Performance of a Microwave Tomography System for Biomedical Applications”, submitted in April 2025. Thesis advisor is Dr. Habib ur Rehman and thesis co-advisors are Dr. Usman Tariq and Dr. Ammar Hasan. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_6c2cc2b63330fd231456549add5592ca |
| identifier_str_mv | 35.232-2025.07 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/26145 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable TransportationAhmed, ShoaibElectric vehiclesLithium-ion batteriesInduction motorField-oriented controlReinforcement learningFuzzy logic controllerA Master of Science thesis in Electrical Engineering by Shoaib Ahmed entitled, “Optimizing the Performance of a Microwave Tomography System for Biomedical Applications”, submitted in April 2025. Thesis advisor is Dr. Habib ur Rehman and thesis co-advisors are Dr. Usman Tariq and Dr. Ammar Hasan. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Increased dependency on fossil fuels as a source of energy has contributed significantly to environmental pollution and rising global temperatures. The automotive industry is making a transition from internal combustion engine (ICE) vehicles that use Gasoline, diesel, or other fossil fuel-dependent energy sources towards battery electric vehicles (BEV) that use an electric motor that can be powered by cleaner modes of energy. However, a significant impediment of BEVs is that lithium-ion batteries have about 100 times less energy density than fossil fuels such as gasoline in terms of both weight and volume. Therefore, a need exists to optimize the performance of lithium-ion batteries to elongate their lifetime. In BEVs, commonly lithium-ion batteries power a field-oriented induction motor drive system that propels the vehicle. The objective of this work is to improve the performance of an indirect field-oriented (IFO) induction motor drive system using three different control approaches and compare their performance in terms of efficient speed regulation and battery energy consumption. The indirect field-oriented control (IFOC) comprises of three control loops: two inner loops control currents while one outer loop regulates the motor speed. The speed control loop is the focus of this study. Firstly, a baseline is developed using a PI controller, which is then replaced by a fuzzy logic controller (FLC) and a reinforcement learning (RL) agent. The FLC and RL above fall under the umbrella of artificial intelligence (AI). Thus, the study aims to investigate and validate that AI can be used to improve the performance of electric vehicles by improving speed regulation, reducing battery energy consumption, and thus increasing the driving range and, hence, the battery lifetime. Also, this study investigates the effect of different control techniques on the battery temperature.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE)Rehman, Habib-urTariq, UsmanHasan, Ammar2025-06-24T09:40:33Z2025-06-24T09:40:33Z2025-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2025.07https://hdl.handle.net/11073/26145en_USoai:repository.aus.edu:11073/261452025-09-11T05:52:32Z |
| spellingShingle | Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable Transportation Ahmed, Shoaib Electric vehicles Lithium-ion batteries Induction motor Field-oriented control Reinforcement learning Fuzzy logic controller |
| status_str | publishedVersion |
| title | Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable Transportation |
| title_full | Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable Transportation |
| title_fullStr | Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable Transportation |
| title_full_unstemmed | Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable Transportation |
| title_short | Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable Transportation |
| title_sort | Artificial Intelligence Driven Electric Vehicle Traction System for Sustainable Transportation |
| topic | Electric vehicles Lithium-ion batteries Induction motor Field-oriented control Reinforcement learning Fuzzy logic controller |
| url | https://hdl.handle.net/11073/26145 |