A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning Technique
In this paper, a hybrid scheme of Dynamic wireless charging (DWC) for electric vehicles EV(s) is proposed to resolve this issue in a network topological infrastructure. The proposed hybrid scheme uses different parameters to allow DWC in EVs. The network infrastructure was established through an enh...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9223665/ |
| _version_ | 1818407790247411712 |
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| author | Muhammad Adil Jehad Ali Qui Thanh Hoai Ta Muhammad Attique Tae-Sun Chung |
| author_facet | Muhammad Adil Jehad Ali Qui Thanh Hoai Ta Muhammad Attique Tae-Sun Chung |
| author_sort | Muhammad Adil |
| collection | DOAJ |
| description | In this paper, a hybrid scheme of Dynamic wireless charging (DWC) for electric vehicles EV(s) is proposed to resolve this issue in a network topological infrastructure. The proposed hybrid scheme uses different parameters to allow DWC in EVs. The network infrastructure was established through an enhanced destination sequential distance vector (Enhanced-DSDV) protocol for participating EVs. The DWC charge between paired EV(s) was enabled by magnetic coupling, where the Charge State Estimator (CSE) was used as an unsupervised machine learning technique to learn the current charging status of each EV. Similarly, the captured data of CSE is shared via embedded wireless nodes in the network following enhanced-DSDV routing protocol. Moreover, the proposed model enables each participating EV to transfer charge to another EV participating in the network in DWC environment. To allow, the drivers to monitor the participating EVs in close proximity with their current charge status, location, and distance information, we have have used a dashboard screen in each EV. In addition, each EV uses a generator to produce a magnetic field for magnetic coupling between paired EV(s) to exchange power in wireless environment. The feasibility of the proposed model was thoroughly examined in the real environment of DWC. The results show that the proposed scheme is reliable in terms of DWC in both static and dynamic. Moreover, the enhanced-DSDV routing protocol performed significantly well than existing schemes particularly in terms of throughput, packet lost ratio and latency. |
| first_indexed | 2024-12-14T09:33:26Z |
| format | Article |
| id | doaj.art-681abaa618564d809f75eae5c773c53c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T09:33:26Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-681abaa618564d809f75eae5c773c53c2022-12-21T23:08:00ZengIEEEIEEE Access2169-35362020-01-01818793318794710.1109/ACCESS.2020.30311829223665A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning TechniqueMuhammad Adil0https://orcid.org/0000-0003-4494-8576Jehad Ali1https://orcid.org/0000-0002-0589-7924Qui Thanh Hoai Ta2https://orcid.org/0000-0002-7622-7338Muhammad Attique3https://orcid.org/0000-0002-7237-180XTae-Sun Chung4https://orcid.org/0000-0001-5992-1136Department of Computer Science, Virtual University of Pakistan, Lahore, PakistanDepartment of Computer Engineering, Ajou University, Suwon, South KoreaInstitute of Research and Development, Duy Tan University, Danang, VietnamDepartment of Software, Sejong University, Seoul, South KoreaDepartment of Computer Engineering, Ajou University, Suwon, South KoreaIn this paper, a hybrid scheme of Dynamic wireless charging (DWC) for electric vehicles EV(s) is proposed to resolve this issue in a network topological infrastructure. The proposed hybrid scheme uses different parameters to allow DWC in EVs. The network infrastructure was established through an enhanced destination sequential distance vector (Enhanced-DSDV) protocol for participating EVs. The DWC charge between paired EV(s) was enabled by magnetic coupling, where the Charge State Estimator (CSE) was used as an unsupervised machine learning technique to learn the current charging status of each EV. Similarly, the captured data of CSE is shared via embedded wireless nodes in the network following enhanced-DSDV routing protocol. Moreover, the proposed model enables each participating EV to transfer charge to another EV participating in the network in DWC environment. To allow, the drivers to monitor the participating EVs in close proximity with their current charge status, location, and distance information, we have have used a dashboard screen in each EV. In addition, each EV uses a generator to produce a magnetic field for magnetic coupling between paired EV(s) to exchange power in wireless environment. The feasibility of the proposed model was thoroughly examined in the real environment of DWC. The results show that the proposed scheme is reliable in terms of DWC in both static and dynamic. Moreover, the enhanced-DSDV routing protocol performed significantly well than existing schemes particularly in terms of throughput, packet lost ratio and latency.https://ieeexplore.ieee.org/document/9223665/Electric vehiclesnetwork topologycharging estimatorelectric generatormachine learningenhanced-DSDV routing protocol |
| spellingShingle | Muhammad Adil Jehad Ali Qui Thanh Hoai Ta Muhammad Attique Tae-Sun Chung A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning Technique IEEE Access Electric vehicles network topology charging estimator electric generator machine learning enhanced-DSDV routing protocol |
| title | A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning Technique |
| title_full | A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning Technique |
| title_fullStr | A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning Technique |
| title_full_unstemmed | A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning Technique |
| title_short | A Reliable Sensor Network Infrastructure for Electric Vehicles to Enable Dynamic Wireless Charging Based on Machine Learning Technique |
| title_sort | reliable sensor network infrastructure for electric vehicles to enable dynamic wireless charging based on machine learning technique |
| topic | Electric vehicles network topology charging estimator electric generator machine learning enhanced-DSDV routing protocol |
| url | https://ieeexplore.ieee.org/document/9223665/ |
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