Design and Experimental Validation of Wireless Electric Vehicle Charger Control Using Genetic Algorithms and Feedforward Artificial Neural Network
Integrating electric vehicles (EVs) into the transportation ecosystem is crucial for environmental protection. With the increasing demand for sustainable mobility solutions, wireless power transfer (WPT) systems present a promising method to facilitate the adoption of EVs while reducing carbon footp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/6/3/43 |
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| author | Marouane El Ancary Abdellah Lassioui Hassan El Fadil Yassine El Asri Anwar Hasni Soukaina Nady |
| author_facet | Marouane El Ancary Abdellah Lassioui Hassan El Fadil Yassine El Asri Anwar Hasni Soukaina Nady |
| author_sort | Marouane El Ancary |
| collection | DOAJ |
| description | Integrating electric vehicles (EVs) into the transportation ecosystem is crucial for environmental protection. With the increasing demand for sustainable mobility solutions, wireless power transfer (WPT) systems present a promising method to facilitate the adoption of EVs while reducing carbon footprints. This paper presents a control strategy for the primary side of a WPT charger utilizing a genetic algorithm (GA) combined with a feedforward artificial neural network (ANN). The aim is to optimize charging in constant current (CC) mode and enhance energy transmission efficiency. The proposed approach employs a GA to control the WPT charger, enabling real-time adaptation of charging parameters. The ANN estimates the system’s efficiency, ensuring optimal performance during the charging process. The developed control strategy significantly improved energy transfer efficiency and system stability. Simulation results demonstrate the effectiveness of this new approach, achieving an efficiency of 89.32% in challenging situations of loss of communication with the vehicle. To validate the design procedure, an experimental prototype was constructed, operating at an operational frequency of 85 kHz. Experimental results confirm the proposed design methodology. |
| format | Article |
| id | doaj-art-7923c67a864841c1b7d1eb9a98e2cc62 |
| institution | OA Journals |
| issn | 2673-4117 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj-art-7923c67a864841c1b7d1eb9a98e2cc622025-08-20T02:11:18ZengMDPI AGEng2673-41172025-02-01634310.3390/eng6030043Design and Experimental Validation of Wireless Electric Vehicle Charger Control Using Genetic Algorithms and Feedforward Artificial Neural NetworkMarouane El Ancary0Abdellah Lassioui1Hassan El Fadil2Yassine El Asri3Anwar Hasni4Soukaina Nady5Ingénierie des Systèmes Avancés (ISA) Laboratory, National School of Applied Sciences (ENSA), Ibn Tofail University, Kenitra 14000, MoroccoIngénierie des Systèmes Avancés (ISA) Laboratory, National School of Applied Sciences (ENSA), Ibn Tofail University, Kenitra 14000, MoroccoIngénierie des Systèmes Avancés (ISA) Laboratory, National School of Applied Sciences (ENSA), Ibn Tofail University, Kenitra 14000, MoroccoIngénierie des Systèmes Avancés (ISA) Laboratory, National School of Applied Sciences (ENSA), Ibn Tofail University, Kenitra 14000, MoroccoIngénierie des Systèmes Avancés (ISA) Laboratory, National School of Applied Sciences (ENSA), Ibn Tofail University, Kenitra 14000, MoroccoIngénierie des Systèmes Avancés (ISA) Laboratory, National School of Applied Sciences (ENSA), Ibn Tofail University, Kenitra 14000, MoroccoIntegrating electric vehicles (EVs) into the transportation ecosystem is crucial for environmental protection. With the increasing demand for sustainable mobility solutions, wireless power transfer (WPT) systems present a promising method to facilitate the adoption of EVs while reducing carbon footprints. This paper presents a control strategy for the primary side of a WPT charger utilizing a genetic algorithm (GA) combined with a feedforward artificial neural network (ANN). The aim is to optimize charging in constant current (CC) mode and enhance energy transmission efficiency. The proposed approach employs a GA to control the WPT charger, enabling real-time adaptation of charging parameters. The ANN estimates the system’s efficiency, ensuring optimal performance during the charging process. The developed control strategy significantly improved energy transfer efficiency and system stability. Simulation results demonstrate the effectiveness of this new approach, achieving an efficiency of 89.32% in challenging situations of loss of communication with the vehicle. To validate the design procedure, an experimental prototype was constructed, operating at an operational frequency of 85 kHz. Experimental results confirm the proposed design methodology.https://www.mdpi.com/2673-4117/6/3/43genetic algorithmsfeedforward ANNelectric vehiclesbattery chargingwireless power transfer |
| spellingShingle | Marouane El Ancary Abdellah Lassioui Hassan El Fadil Yassine El Asri Anwar Hasni Soukaina Nady Design and Experimental Validation of Wireless Electric Vehicle Charger Control Using Genetic Algorithms and Feedforward Artificial Neural Network Eng genetic algorithms feedforward ANN electric vehicles battery charging wireless power transfer |
| title | Design and Experimental Validation of Wireless Electric Vehicle Charger Control Using Genetic Algorithms and Feedforward Artificial Neural Network |
| title_full | Design and Experimental Validation of Wireless Electric Vehicle Charger Control Using Genetic Algorithms and Feedforward Artificial Neural Network |
| title_fullStr | Design and Experimental Validation of Wireless Electric Vehicle Charger Control Using Genetic Algorithms and Feedforward Artificial Neural Network |
| title_full_unstemmed | Design and Experimental Validation of Wireless Electric Vehicle Charger Control Using Genetic Algorithms and Feedforward Artificial Neural Network |
| title_short | Design and Experimental Validation of Wireless Electric Vehicle Charger Control Using Genetic Algorithms and Feedforward Artificial Neural Network |
| title_sort | design and experimental validation of wireless electric vehicle charger control using genetic algorithms and feedforward artificial neural network |
| topic | genetic algorithms feedforward ANN electric vehicles battery charging wireless power transfer |
| url | https://www.mdpi.com/2673-4117/6/3/43 |
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