Integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles
The limited driving range of BEVs is the main challenge in developing zero-emission Battery Electric Vehicles (BEVs) to replace traditional fuel-based vehicles. This limitation necessitates an increase in battery energy while balancing the power supply and consumption requirements for the vehicle...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universiti Putra Malaysia Press
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/109227/ http://psasir.upm.edu.my/id/eprint/109227/1/17%20JST-4469-2023.pdf |
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| author | Abulifa, Abdulhadi Abdulsalam Che Soh, Azura Hassan, Mohd Khair Raja Kamil Mohd Radzi, Mohd Amran |
| author_facet | Abulifa, Abdulhadi Abdulsalam Che Soh, Azura Hassan, Mohd Khair Raja Kamil Mohd Radzi, Mohd Amran |
| author_sort | Abulifa, Abdulhadi Abdulsalam |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The limited driving range of BEVs is the main challenge in developing zero-emission
Battery Electric Vehicles (BEVs) to replace traditional fuel-based vehicles. This
limitation necessitates an increase in battery energy while balancing the power supply and
consumption requirements for the vehicle’s motor and auxiliaries, such as the Heating,
Ventilation, and Air Conditioning (HVAC) system. This research proposes a solution to
achieve more efficient control of HVAC consumption by integrating fuzzy logic techniques
with brute-force algorithms to optimize the Energy Management System (EMS) in BEVs.
The model was based on actual parameters, implemented using MATLAB-Simulink and
ADVISOR software, and configured using a backward-facing design incorporating the
technical specifications of a Malaysian electric car, the PROTON IRIZ. An optimal solution
was proposed based on the Satisfaction Ratio (SR) and State of Charge (SoC) metrics to
achieve the best system optimization. The results demonstrate that the optimized fuzzy
EMS improved power consumption by 23.2% to 26.6% compared to a basic fuzzy EMS.
The proposed solution significantly improves the driving range of BEVs. |
| first_indexed | 2025-11-15T14:02:46Z |
| format | Article |
| id | upm-109227 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:02:46Z |
| publishDate | 2023 |
| publisher | Universiti Putra Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1092272024-10-14T07:34:31Z http://psasir.upm.edu.my/id/eprint/109227/ Integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles Abulifa, Abdulhadi Abdulsalam Che Soh, Azura Hassan, Mohd Khair Raja Kamil Mohd Radzi, Mohd Amran The limited driving range of BEVs is the main challenge in developing zero-emission Battery Electric Vehicles (BEVs) to replace traditional fuel-based vehicles. This limitation necessitates an increase in battery energy while balancing the power supply and consumption requirements for the vehicle’s motor and auxiliaries, such as the Heating, Ventilation, and Air Conditioning (HVAC) system. This research proposes a solution to achieve more efficient control of HVAC consumption by integrating fuzzy logic techniques with brute-force algorithms to optimize the Energy Management System (EMS) in BEVs. The model was based on actual parameters, implemented using MATLAB-Simulink and ADVISOR software, and configured using a backward-facing design incorporating the technical specifications of a Malaysian electric car, the PROTON IRIZ. An optimal solution was proposed based on the Satisfaction Ratio (SR) and State of Charge (SoC) metrics to achieve the best system optimization. The results demonstrate that the optimized fuzzy EMS improved power consumption by 23.2% to 26.6% compared to a basic fuzzy EMS. The proposed solution significantly improves the driving range of BEVs. Universiti Putra Malaysia Press 2023-03 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/109227/1/17%20JST-4469-2023.pdf Abulifa, Abdulhadi Abdulsalam and Che Soh, Azura and Hassan, Mohd Khair and Raja Kamil and Mohd Radzi, Mohd Amran (2023) Integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles. Pertanika Journal of Science and Technology, 32 (2). pp. 797-817. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-4469-2023 10.47836/pjst.32.2.17 |
| spellingShingle | Abulifa, Abdulhadi Abdulsalam Che Soh, Azura Hassan, Mohd Khair Raja Kamil Mohd Radzi, Mohd Amran Integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles |
| title | Integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles |
| title_full | Integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles |
| title_fullStr | Integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles |
| title_full_unstemmed | Integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles |
| title_short | Integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles |
| title_sort | integrating fuzzy logic and brute force algorithm in optimizing energy management systems for battery electric vehicles |
| url | http://psasir.upm.edu.my/id/eprint/109227/ http://psasir.upm.edu.my/id/eprint/109227/ http://psasir.upm.edu.my/id/eprint/109227/ http://psasir.upm.edu.my/id/eprint/109227/1/17%20JST-4469-2023.pdf |