Machine learning‐based charge scheduling of electric vehicles with minimum waiting time

Vanitha, V.; Resmi, R; Reddy, Karri Naga Sai Vineela

doi:10.1111/coin.12333

Cited by 13 publications

(2 citation statements)

References 15 publications

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“…Besides, these ML algorithms provide lesser complexity when integrating with EVs and produce a result in the fastest manner with lesser computational power. Vanitha et al have proposed the ML‐based charge scheduling of EVs with the minimum waiting time 24 . The fast‐charging port identified through the analysis of different routes are available with the destination.…”

Section: Related Workmentioning

confidence: 99%

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An energy‐efficient blockchain approach for secure communication in IoT‐enabled electric vehicles

Bhaskar¹,

Prasanth

Saranya³

2022

Int J Communication

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In today's world, electric vehicles (EVs) play a significant role in transportation automation systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV generally depends on electric charges where the appropriate usage, charging, and energy management are the key constraints in EVs. To overcome these issues, proper energy management is essential in current EV management. In this paper, a novel blockchain-based secure energy management has been proposed to provide efficient energy management in transportation automation. Primarily, the EVs have connected to the Internet of Things (IoT) sensors for collecting information like charging level, distance to be traveled, and location of the EVs. This information has been processed by an information center and transferred to the random forest classifier to identify the price of charging. Afterward, it can be transferred to the power scheduling algorithm for finding the nearest charging location (shortest distance) and time of charging to a specific EV. Finally, this information is stored in blocks to mitigate the misleading of EVs and to offer secure price transactions between the users and charging stations. The results manifest that the proposed scheme provides improved EV management with 94.5% of accuracy and maintains 10% lesser communication overhead as compared with existing state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

“…Vanitha et al have proposed the ML-based charge scheduling of EVs with the minimum waiting time. 24 The fast-charging port identified through the analysis of different routes are available with the destination. The existing power demand is considered for analysis, and the new arrivals are not taken for analysis.…”

Section: Algorithms In Ev Energy Managementmentioning

confidence: 99%