Open Access

ARTICLE

Study on Two-Tier EV Charging Station Recommendation Strategy under Multi-Factor Influence

Miao Liu, Lei Feng, Yexun Yuan, Ye Liu, Peng Geng^*

School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167, China

* Corresponding Author: Peng Geng. Email:

Journal on Artificial Intelligence 2023, 5, 181-193. https://doi.org/10.32604/jai.2023.046066

Received 17 September 2023; Accepted 27 November 2023; Issue published 28 December 2023

Abstract

This article aims to address the clustering effect caused by unorganized charging of electric vehicles by adopting a two-tier recommendation method. The electric vehicles (EVs) are classified into high-level alerts and general alerts based on their state of charge (SOC). EVs with high-level alerts have the most urgent charging needs, so the distance to charging stations is set as the highest priority for recommendations. For users with general alerts, a comprehensive EV charging station recommendation model is proposed, taking into account factors such as charging price, charging time, charging station preference, and distance to the charging station. Using real data from EV charging stations and ride-hailing vehicles in Xiamen City, Fujian Province, simulation analyses are conducted using Python for different periods of the day. The research results show that the stability of the multi-factor recommendation model in terms of service density variance, coverage rate, price cost, and distance cost outperform single-factor models. This indicates that our composite multi-factor recommendation model has significant practical value in resolving the clustering phenomenon caused by unorganized EV charging, optimizing the EV charging service system, and improving user satisfaction.

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Keywords

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