Page 337 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD)
Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies
Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470

Real-Time Electric Vehicle Charger Availability:

A Study on ChargeHub's Intelligent Network Design

Chetan Kaner , Tanmay Pawar , Prof. Usha Kosarkar
2
1
3
1,2,3 Department of Science and Technology,
1,2 G H Raisoni Institute of Engineering and Technology, Nagpur, Maharashtra, India
3 G H Raisoni College of Engineering and Management, Nagpur, Maharashtra, India

ABSTRACT III. SYSTEM ARCHITECTURE
The rapid adoption of electric vehicles (EVs) necessitates ChargeHub's architecture is designed to ensure scalability,
efficient and intelligent charging infrastructures. reliability, and real-time responsiveness. The key
ChargeHub, an advanced network for real-time EV charger components include:
management, integrates cutting-edge technologies such as 1. IoT-Enabled Charging Stations: Equipped with
IoT, cloud computing, and AI to ensure charger availability sensors, these stations monitor parameters such as
and optimize usage. This study explores ChargeHub's charger status, energy consumption, and queue lengths.
intelligent network design, detailing its architecture, data 2. Cloud-Based Data Management: A centralized cloud
flow mechanisms, and optimization strategies. The paper platform aggregates and processes data from all
further evaluates the impact of real-time data integration
on charger accessibility, user satisfaction, and network connected stations, enabling seamless integration and
analysis.
reliability. Results indicate that ChargeHub's intelligent
design significantly enhances charging efficiency, reduces 3. AI Algorithms: Predictive models analyze historical and
wait times, and supports the scalable deployment of EV real-time data to forecast demand, optimize resource
charging solutions. allocation, and provide recommendations.

4. User Interfaces: Mobile applications and web
KEYWORDS: Electric Vehicles, Real-Time Charging,
ChargeHub, IoT, AI, Charging Infrastructure, Cloud Computing dashboards offer real-time updates, reservation
capabilities, and personalized notifications to users.
I. INTRODUCTION IV. DATA FLOW AND REAL-TIME UPDATES
The global shift towards electric mobility has catalyzed the Data flow within ChargeHub’s network is streamlined to
demand for robust and efficient EV charging networks. minimize latency and ensure accuracy. The process involves:
Traditional charging systems often suffer from limitations 1. Data Collection: IoT sensors capture real-time data,
such as uneven charger distribution, lack of real-time including charger availability, usage patterns, and
availability updates, and long wait times. These inefficiencies energy metrics.
hinder the seamless adoption of EVs, posing challenges for
drivers and network operators alike. 2. Data Transmission: Collected data is transmitted to the
cloud using secure communication protocols such as
ChargeHub, an intelligent network for EV chargers, MQTT and HTTPS.
addresses these challenges by leveraging real-time data
integration, predictive analytics, and dynamic resource 3. Data Processing: The cloud platform processes raw
allocation. By providing users with real-time charger data, identifying trends and generating actionable
availability, ChargeHub enhances the EV charging experience insights.
and optimizes network efficiency. This paper delves into the 4. User Notification: Processed data is relayed to user
technical and operational aspects of ChargeHub's design, interfaces, ensuring real-time updates and notifications.
highlighting its role in addressing critical gaps in existing EV
charging infrastructures. V. OPTIMIZATION STRATEGIES
ChargeHub employs several optimization techniques to
II. RELATED WORK enhance network efficiency and user experience:
Several studies have explored the integration of IoT and AI in 1. Dynamic Load Balancing: Ensures equitable
EV charging systems. Research by Li et al. (2021) highlights distribution of charging demand across stations,
the potential of IoT in real-time charger monitoring and reducing congestion.
management. Similarly, a study by Smith et al. (2020)
demonstrates how predictive analytics can reduce charging 2. Predictive Analytics: Anticipates peak demand periods
station congestion and improve user satisfaction. and adjusts resource allocation proactively.
ChargeHub builds on these advancements by integrating 3. Energy Management: Balances grid load by integrating
cloud computing, AI-driven predictive algorithms, and user- renewable energy sources and utilizing off-peak
centric mobile applications. Unlike traditional networks that charging strategies.
rely on static data, ChargeHub’s approach combines real- 4. Reservation System: Allows users to reserve charging
time data streams with advanced optimization techniques, slots, minimizing wait times and improving planning.
ensuring a dynamic and adaptive charging network.

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