Integrating AI Techniques for Enhanced Management and Operation of Electric Vehicle Charging Stations

被引:0
|
作者
Cardoso, Filipe [1 ]
Rosado, Jose [3 ]
Silva, Marco [3 ]
Martins, Pedro [2 ]
Vaz, Paulo [2 ]
Silva, Jose [2 ]
Abbasi, Maryam [3 ]
机构
[1] Polytech Inst Santarem, Santarem Higher Sch Management & Technol, Porto, Portugal
[2] Polytech Viseu, CISeD Digital Serv Res Ctr, Viseu, Portugal
[3] Coimbra Polytech, Appl Res Inst, Coimbra, Portugal
来源
NEW TRENDS IN DISRUPTIVE TECHNOLOGIES, TECH ETHICS, AND ARTIFICIAL INTELLIGENCE, DITTET 2024 | 2024年 / 1459卷
关键词
Electric Vehicle (EV); Charging Stations; Intelligent Charging Controller; Predictive Charging; Dynamic Pricing;
D O I
10.1007/978-3-031-66635-3_1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Managing Electric Vehicle (EV) charging stations poses challenges due to limited contracted power. To address this, we introduce the Intelligent Electric Vehicle Charging Controller (IEVCC) prototype. IEVCC optimizes power utilization without additional costs and can operate independently or in a network. A designated manager allocates power among chargers through load balancing and prioritization. Our system incorporates predictive charging, using AI to anticipate users' needs based on historical data and preferences, ensuring availability. Additionally, AI-driven load balancing efficiently distributes power among chargers, preventing overload. Dynamic pricing strategies incentivize efficient charging by adjusting tariffs based on demand and available power, promoting sustainability In conclusion, the IEVCC offers a holistic solution for managing EV charging stations. By optimizing power usage, integrating predictive charging, load balancing, and dynamic pricing, it enhances efficiency and ensures prudent resource utilization in the transition towards sustainable transportation.
引用
收藏
页码:3 / 14
页数:12
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