Page 406 - 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

Smart Charging Solutions: Leveraging ChargeHub
for Real-Time Electric Vehicle Charger Monitoring

1
Moreshwar Dhoke , Vedanshu Lohi , Prof. Usha Kosakar
2
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 II. RELATED WORK
With the rise of electric vehicles (EVs), efficient charging The growing body of research highlights the importance of
infrastructure has become critical to support the transition smart charging solutions for enhancing EV charging
to a sustainable future. ChargeHub, a state-of-the-art infrastructure. Several studies have focused on the
solution for real-time electric vehicle charger monitoring, integration of IoT technologies for monitoring and managing
offers advanced features like dynamic load management, energy distribution.
fault detection, and user analytics to optimize the EV A. IoT-Based Charging Networks
charging process. This paper explores how ChargeHub IoT has been a game-changer in the EV charging ecosystem.
leverages IoT technologies, big data analytics, and cloud Researchers in [1] emphasized the role of IoT in enabling
computing to ensure seamless monitoring and enhance real-time data acquisition and monitoring, facilitating better
user experience. With its predictive analytics capabilities, resource allocation.
ChargeHub facilitates proactive maintenance, reducing
downtime and improving overall reliability. This study B. Predictive Analytics for Charger Maintenance
presents an in-depth analysis of ChargeHub’s architecture, Predictive analytics has proven effective in minimizing
features, and its impact on EV adoption, aiming to address downtime by identifying potential faults before they occur. A
the challenges of scalability and interoperability in EV study in [2] demonstrated how machine learning algorithms
charging networks. could predict charger failures, reducing maintenance costs

and improving user satisfaction.
KEYWORDS: Electric Vehicles, Smart Charging, IoT,
C. Scalability Challenges in EV Networks
ChargeHub, Real-Time Monitoring, Predictive Analytics, Cloud Scalability remains a major challenge in deploying large-
Computing
scale EV charging networks. The authors in [3] proposed a
decentralized architecture to address this issue, ensuring
I. INTRODUCTION efficient load management and reduced latency.
The widespread adoption of electric vehicles (EVs) has
driven the demand for robust and efficient charging D. Interoperability Standards
infrastructure. Traditional charging networks face challenges Ensuring interoperability across different charger
such as inconsistent availability, unreliable monitoring manufacturers is critical for creating a seamless user
systems, and limited user insights. Smart charging solutions experience. Studies in [4] explored the adoption of Open
like ChargeHub address these gaps by integrating Internet of Charge Point Protocol (OCPP) as a universal standard for
Things (IoT) devices, data analytics, and real-time achieving this goal.
monitoring systems to enhance the user experience and III. PROPOSED WORK
optimize energy distribution. The proposed work focuses on analyzing ChargeHub’s
ChargeHub, as a comprehensive solution, ensures real-time capabilities in real-time charger monitoring and its impact
monitoring of chargers, predictive fault detection, and on EV adoption. The framework includes:
dynamic load balancing. This paper aims to explore the A. Real-Time Monitoring
architecture and benefits of ChargeHub, emphasizing its role ChargeHub employs IoT-enabled sensors to monitor charger
in addressing challenges like high energy demand, network status, energy usage, and fault conditions in real time. The
scalability, and interoperability among different EV charger collected data is processed in the cloud to provide actionable
manufacturers. Through this analysis, we aim to insights to users and operators.
demonstrate how such systems can accelerate the transition
to a sustainable and user-centric EV ecosystem. B. Dynamic Load Management
Dynamic load management is critical for optimizing energy
The objectives of this study include: distribution across multiple chargers. ChargeHub’s
1. Examining the architectural framework of ChargeHub. algorithms prioritize charging based on energy demand,
2. Identifying key features and their benefits. ensuring efficient utilization of available resources.
3. Exploring the challenges of scalability and C. Predictive Maintenance
interoperability. By analyzing historical data, ChargeHub predicts potential
failures, enabling proactive maintenance. This reduces
4. Demonstrating the potential impact of real-time downtime and extends the lifespan of charging equipment.
monitoring on EV adoption.

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 396