Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehicles
The automotive sector is experiencing rapid evolution, with the next-generation emphasizing clean energy sources such as fuel-cell hybrid electric vehicles (FCHEVs) due to their energy efficiency, eco-friendliness, and extended driving distance. Implementing effective energy management strategies pl...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-03-01
|
| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1344341/full |
| _version_ | 1797257590814539776 |
|---|---|
| author | Motab Turki Almousa Hegazy Rezk Ali Alahmer Ali Alahmer |
| author_facet | Motab Turki Almousa Hegazy Rezk Ali Alahmer Ali Alahmer |
| author_sort | Motab Turki Almousa |
| collection | DOAJ |
| description | The automotive sector is experiencing rapid evolution, with the next-generation emphasizing clean energy sources such as fuel-cell hybrid electric vehicles (FCHEVs) due to their energy efficiency, eco-friendliness, and extended driving distance. Implementing effective energy management strategies play a critical role in optimizing power flow and electrical efficiency in these vehicles. This study proposes an optimized energy management strategy (EMS) for FCHEVs. The suggested EMS introduces a hybridization between the equivalent consumption minimization strategy (ECMS) and the Artificial Hummingbird Algorithm (AHA). The Federal Test Procedure for Urban Driving (FTP-75) is employed to evaluate the performance of the proposed EMS. The results are assessed and validated through comparison with outcomes obtained by other algorithms. The findings demonstrate that the proposed EMS surpasses other optimizers in reducing fuel consumption, potentially achieving a 48.62% reduction. Moreover, the suggested EMS also yields a 15.45% increase in overall system efficiency. |
| first_indexed | 2024-04-24T22:40:04Z |
| format | Article |
| id | doaj.art-22d883da78b042baa298d24899846d10 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-24T22:40:04Z |
| publishDate | 2024-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-22d883da78b042baa298d24899846d102024-03-19T04:38:13ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-03-011210.3389/fenrg.2024.13443411344341Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehiclesMotab Turki Almousa0Hegazy Rezk1Ali Alahmer2Ali Alahmer3Department of Electrical Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Mechanical Engineering, Tuskegee University, Tuskegee, AL, United StatesDepartment of Mechanical Engineering, Faculty of Engineering, Tafila Technical University, Tafila, JordanThe automotive sector is experiencing rapid evolution, with the next-generation emphasizing clean energy sources such as fuel-cell hybrid electric vehicles (FCHEVs) due to their energy efficiency, eco-friendliness, and extended driving distance. Implementing effective energy management strategies play a critical role in optimizing power flow and electrical efficiency in these vehicles. This study proposes an optimized energy management strategy (EMS) for FCHEVs. The suggested EMS introduces a hybridization between the equivalent consumption minimization strategy (ECMS) and the Artificial Hummingbird Algorithm (AHA). The Federal Test Procedure for Urban Driving (FTP-75) is employed to evaluate the performance of the proposed EMS. The results are assessed and validated through comparison with outcomes obtained by other algorithms. The findings demonstrate that the proposed EMS surpasses other optimizers in reducing fuel consumption, potentially achieving a 48.62% reduction. Moreover, the suggested EMS also yields a 15.45% increase in overall system efficiency.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1344341/fullelectric vehicleenergy managementequivalent consumption minimization strategyfuel-cell hybrid electric vehiclesoptimization |
| spellingShingle | Motab Turki Almousa Hegazy Rezk Ali Alahmer Ali Alahmer Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehicles Frontiers in Energy Research electric vehicle energy management equivalent consumption minimization strategy fuel-cell hybrid electric vehicles optimization |
| title | Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehicles |
| title_full | Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehicles |
| title_fullStr | Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehicles |
| title_full_unstemmed | Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehicles |
| title_short | Optimized equivalent consumption minimization strategy-based artificial Hummingbird Algorithm for electric vehicles |
| title_sort | optimized equivalent consumption minimization strategy based artificial hummingbird algorithm for electric vehicles |
| topic | electric vehicle energy management equivalent consumption minimization strategy fuel-cell hybrid electric vehicles optimization |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1344341/full |
| work_keys_str_mv | AT motabturkialmousa optimizedequivalentconsumptionminimizationstrategybasedartificialhummingbirdalgorithmforelectricvehicles AT hegazyrezk optimizedequivalentconsumptionminimizationstrategybasedartificialhummingbirdalgorithmforelectricvehicles AT alialahmer optimizedequivalentconsumptionminimizationstrategybasedartificialhummingbirdalgorithmforelectricvehicles AT alialahmer optimizedequivalentconsumptionminimizationstrategybasedartificialhummingbirdalgorithmforelectricvehicles |