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July 7, 2025· 2025 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)
conference-paper

The Design of Blockchain Smart Contract for the Electric Vehicle’s Charging Station Nearby

Authors:Srijidtra MahapakulchaiPannawat Khongdee

Abstract

Recently, the smart grid has been proposed as the solution to our sustainable source of electricity. Along with the blockchain network as a tool for the decentralized power generation management platform, we can achieve peer-to-peer energy trading. In this research, we emphasize the design of a blockchain smart contract for searching the closet and available electric vehicle charging stations. In the future, each house with solar cell rooftops can generate enough electric power to provide charging service. Therefore the charging stations will eventually be scattered in both rural and urban areas. If we use blockchain technology, people can generate electricity themselves and turn their homes into charging stations, saving money directly without going through intermediary banks. However, to implement this system, there are still many challenges such as long charging time, the unavailability of charging stations, searching and reserving charging systems, etc. The algorithm design concept is based on the idea of bidding. The proposed smart contract algorithm design and development is used as an application to recommend charging stations that are available and closest to electric vehicles by applying smart contracts and blockchain technology. Each station participates in the bidding process by specifying its location and the length of the queue. The smart contract then calculates the distance between the vehicle and the station and checks the waiting time whether it meets the needs of electric vehicle users. Within the bidding time, the smart contract will automatically return the location of the nearest charging station, similar to winning bidders. The experimental results showed that the distance calculations proposed in the smart contracts matched the actual distances that were actually usable by Google for 84 out of 100 rounds of the experiment.

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