Optimal Load Sharing between Lithium-Ion Battery and Supercapacitor for Electric Vehicle Applications
There has been a suggestion for the best energy management method for an electric vehicle with a hybrid power system. The objective is to supply the electric vehicle with high-quality electricity. The hybrid power system comprises a supercapacitor (SC) bank and a lithium-ion battery. The recommended...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/8/201 |
| _version_ | 1827728491190157312 |
|---|---|
| author | Hegazy Rezk Rania M. Ghoniem |
| author_facet | Hegazy Rezk Rania M. Ghoniem |
| author_sort | Hegazy Rezk |
| collection | DOAJ |
| description | There has been a suggestion for the best energy management method for an electric vehicle with a hybrid power system. The objective is to supply the electric vehicle with high-quality electricity. The hybrid power system comprises a supercapacitor (SC) bank and a lithium-ion battery. The recommended energy management plan attempts to maintain the bus voltage while providing the load demand with high-quality power under various circumstances. The management controller is built on a metaheuristic optimization technique that enhances the flatness theory-based controller’s trajectory generation parameters. The SC units control the DC bus while the battery balances the power on the common line. This study demonstrates the expected contribution using particle swarm optimization and performance are assessed under various optimization parameters, including population size and maximum iterations. Their effects on controller performance are examined in the study. The outcomes demonstrate that the number of iterations significantly influences the algorithm’s ability to determine the best controller parameters. The results imply that combining metaheuristic optimization techniques with flatness theory can enhance power quality. The suggested management algorithm ensures power is shared efficiently, protecting power sources and providing good power quality. |
| first_indexed | 2024-03-10T23:29:55Z |
| format | Article |
| id | doaj.art-9682cbdc5b83449c9d81748abc13f6af |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-10T23:29:55Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-9682cbdc5b83449c9d81748abc13f6af2023-11-19T03:24:08ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-07-0114820110.3390/wevj14080201Optimal Load Sharing between Lithium-Ion Battery and Supercapacitor for Electric Vehicle ApplicationsHegazy Rezk0Rania M. Ghoniem1Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Al-Kharj 11942 , Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaThere has been a suggestion for the best energy management method for an electric vehicle with a hybrid power system. The objective is to supply the electric vehicle with high-quality electricity. The hybrid power system comprises a supercapacitor (SC) bank and a lithium-ion battery. The recommended energy management plan attempts to maintain the bus voltage while providing the load demand with high-quality power under various circumstances. The management controller is built on a metaheuristic optimization technique that enhances the flatness theory-based controller’s trajectory generation parameters. The SC units control the DC bus while the battery balances the power on the common line. This study demonstrates the expected contribution using particle swarm optimization and performance are assessed under various optimization parameters, including population size and maximum iterations. Their effects on controller performance are examined in the study. The outcomes demonstrate that the number of iterations significantly influences the algorithm’s ability to determine the best controller parameters. The results imply that combining metaheuristic optimization techniques with flatness theory can enhance power quality. The suggested management algorithm ensures power is shared efficiently, protecting power sources and providing good power quality.https://www.mdpi.com/2032-6653/14/8/201electric vehicleoptimizationLi-ion batterysupercapacitor |
| spellingShingle | Hegazy Rezk Rania M. Ghoniem Optimal Load Sharing between Lithium-Ion Battery and Supercapacitor for Electric Vehicle Applications World Electric Vehicle Journal electric vehicle optimization Li-ion battery supercapacitor |
| title | Optimal Load Sharing between Lithium-Ion Battery and Supercapacitor for Electric Vehicle Applications |
| title_full | Optimal Load Sharing between Lithium-Ion Battery and Supercapacitor for Electric Vehicle Applications |
| title_fullStr | Optimal Load Sharing between Lithium-Ion Battery and Supercapacitor for Electric Vehicle Applications |
| title_full_unstemmed | Optimal Load Sharing between Lithium-Ion Battery and Supercapacitor for Electric Vehicle Applications |
| title_short | Optimal Load Sharing between Lithium-Ion Battery and Supercapacitor for Electric Vehicle Applications |
| title_sort | optimal load sharing between lithium ion battery and supercapacitor for electric vehicle applications |
| topic | electric vehicle optimization Li-ion battery supercapacitor |
| url | https://www.mdpi.com/2032-6653/14/8/201 |
| work_keys_str_mv | AT hegazyrezk optimalloadsharingbetweenlithiumionbatteryandsupercapacitorforelectricvehicleapplications AT raniamghoniem optimalloadsharingbetweenlithiumionbatteryandsupercapacitorforelectricvehicleapplications |