Hybrid Renewable Energy Based Intelligent Power for EV Charging Station

Hybrid Renewable Energy Based Intelligent Power for EV Charging Station

Divya dharshini M

Department of Electronics and Electrical Engineering, Sathyabama Institute of science and technology Chennai 600 119

Jershika J

Department of Electronics and Electrical Engineering, Sathyabama Institute of science and technology Chennai 600 119

Sivachidambaranathan V

Professor, EEE Sathyabama Institute of Science and Technology technology Chennai 600 119

Abstract – The increasing acceptance of electric vehicles has driven a parallel demand for efficient and environmentally responsible charging infrastructure. While EV adoption helps lower carbon footprints and decreases reliance on petroleum- based fuels, these advantages may be compromised if charging stations depend largely on electricity generated from conventional sources. Therefore, renewable-powered solutions are essential for sustainable electric mobility.

This study proposes an intelligent hybrid energy framework that merges solar and wind power to provide uninterrupted electricity for EV charging. Because these renewable sources function under varying environmental conditions, their integration strengthens energy reliability and availability.

At the heart of the system is a sophisticated energy flow controller responsible for coordinating power generation, storage, and delivery. Electricity produced from solar panels and wind turbines is continuously assessed and routed through a smart algorithm that favors renewable usage whenever feasible. Excess energy produced during high-generation intervals is stored in battery systems for later deployment.

When renewable production is insufficient, the controller strategically utilizes stored energy or supplements it with grid electricity to maintain seamless charging operations. This intelligent management improves energy efficiency, prevents wastage, and decreases dependence on traditional power infrastructure. The decision-making process considers factors such as consumption demand, generation trends, battery state of charge, and weather variations.

Additionally, real-time monitoring functions allow comprehensive analysis of energy behavior, enabling predictive control, rapid fault detection, and performance enhancement. The systems safe, adaptable, and scalable design supports installation in diverse settings ranging from urban charging stations to remote off-grid locations.

To summarize, the hybrid renewable intelligent charging system offers a practical, clean, and dependable approach to supporting the expanding EV ecosystem. By combining renewable resources with advanced control techniques, it promotes environmental sustainability and accelerates the transition toward greener transportation.

Keywords Hybrid Renewable Energy System, Electric Vehicle Charging, Solar Energy, Wind Energy, Intelligent Energy Flow Management, Smart Charging Infrastructure, Sustainable Power System, Energy Storage System, Eco-Friendly EV Charging

1. INTRODUCTION

   The rapid electrification of transportation is reshaping mobility worldwide. Factors such as environmental awareness, escalating fuel costs, and regulatory mandates are accelerating the replacement of combustion-engine vehicles with electric alternatives. Despite their advantages, EV deployment requires robust and sustainable charging ecosystems to fully realize their environmental potential.Grid-dependent charging infrastructure, often powered by fossil fuels, increases carbon output and intensifies grid load during high-demand intervals. Anticipated growth in electricity consumption may lead to operational challenges, reinforcing the importance of renewable integration.Solar and wind technologies provide complementary generation profiles that enhance reliability when deployed together. Solar systems perform optimally during daylight, while wind turbines contribute power across varying conditions. Hybridization minimizes intermittency and stabilizes energy delivery.

   Combining these renewable resources with intelligent control architectures enables efficient energy allocation among generation units, storage systems, and charging loads. Real- time analytics guide decision-making processes that favor renewable utilization while preserving surplus energy.Battery storage ensures continuity by compensating for production variability, improving efficiency, and safeguarding system stability through controlled charge management.Furthermore, hybrid renewable charging aligns with smart grid evolution, reduces infrastructure strain, and supports decarbonization initiatives. Such systems are particularly valuable in regions with limited grid access.Therefore, intelligent hybrid renewable EV charging frameworks represent a sustainable, scalable, and future-ready solution that supports the global transition toward clean transportation.

2. RELATED WORK

   Dharmakeerthi [1] proposed that the rapid growth of electric vehicle fast charging can significantly affect power system voltage stability, highlighting the importance of developing efficient and reliable charging infrastructures to maintain grid performance. Mithulananthan emphasized that integrating advanced charging technologies with renewable energy sources can mitigate voltage fluctuations while supporting

   sustainable transportation systems. Saha further explained that improving power transfer efficiency is essential for ensuring stable and secure operation of modern electrical networks.

   Marah [2] proposed that electric vehicle charging systems can create additional stress on low-voltage distribution networks, making it necessary to adopt smarter and more adaptive charging solutions. Bhavanam highlighted that wireless charging technology, when combined with renewable sources such as solar and wind energy, can reduce dependency on traditional grid-based power. Taylor noted that such integration enhances charging convenience while promoting environmentally responsible energy usage. Ekwue [3] further suggested that advanced infrastructure planning is crucial to accommodate the increasing penetration of EVs without compromising network reliability.

   Datta [4] proposed that battery energy storage systems play a critical role in enhancing transient frequency stability in large- scale power systems. Kalam explained that coupling storage solutions with wireless power transmission can ensure continuous energy availability even during fluctuations in renewable generation. Shi added that intelligent energy management techniques can optimize the selection of power sources, improving both efficiency and system resilience.

   Moo [5] proposed a battery power system architecture utilizing arrayed battery modules to improve reliability and performance. Jian observed that managing multiple batteries through intelligent controllers allows efficient distribution of stored renewable energy for wireless charging applications. Wu further indicated that such modular systems support scalability while maintaining operational stability. Yu and Hua concluded that advanced battery configurations can significantly enhance sustainable charging station design.

   Gupta [6] proposed a real-time IV and PV curve tracer to improve the monitoring and performance analysis of solar energy systems. Chauhan emphasized that accurate curve tracing enables better maximum power extraction from photovoltaic sources, which is essential for wireless charging stations powered by renewable energy. Saxena added that integrating monitoring tools with control units such as Arduino helps in selcting the most efficient battery for charging electric vehicles, thereby improving overall system effectiveness.

   Kou [7] proposed that voltage stability analysis based on PQ curves is essential when incorporating wind power into modern electrical grids. Li explained that understanding these characteristics helps maintain consistent power delivery for wireless transmission systems. Their work supports the development of hybrid renewable charging stations capable of delivering stable and eco-friendly energy.

   Liang [8] proposed measurement-based characteristic curves to enhance voltage stability and control at wind power plant interconnection points. Shabbir highlighted that such measurement-driven approaches allow better prediction of system behavior under varying load conditions. Khan and Yan further noted that improved stability assessment contributes to reliable wireless power transmission, especially when renewable sources form the primary energy supply.

   Ashique [9] proposed an adaptive maximum power point tracking technique using sectionalized piece-wise linear PV curves to improve solar energy harvesting efficiency. Salam

   explained that enhanced MPPT methods ensure optimal utilization of renewable power for continuous wireless charging. Ahmed added that efficient energy extraction directly reduces reliance on fossil fuels while supporting sustainable power transmission.

   Owusu-Mireku [10] proposed analyzing the robustness of the closest unstable equilibrium point along a PV curve to better understand system stability limits. Chiang emphasized that identifying these limits helps engineers design resilient charging infrastructures capable of handling dynamic operating conditions. Their research strengthens the foundation for efficient wireless electricity transmission with minimal losses.

   Rahmani proposed an under voltage-frequency load shedding strategy for islanded inverter-based microgrids using power factor-based PV curves. Rezaei-zare explained that this method enhances system protection and prevents large-scale failures during disturbances. Their study supports the creation of cost-effective, reliable, and sustainable wireless charging stations that leverage renewable energy while ensuring safe power distribution.

3. EXISTING SYSTEM

   The increasing adoption of electric vehicles is exerting heavy pressure on current power grids, many of which lack the readiness to support elevated electricity consumption during peak charging intervals. Most charging stations continue to rely on grid electricity, a considerable share of which is produced from fossil fuels. This dependence heightens emissions and weakens the environmental benefits promised by EV technology.

   Extensive charging activity may also lead to grid overload, voltage instability, and greater financial burdens for utility operators. As EV deployment expands, these problems are likely to intensify, underscoring the necessity for sustainable energy alternatives in charging infrastructure. Solar and wind energy are promising candidates because they are renewable and environmentally benign; however, their variability poses challenges for delivering consistent power.

   Solar generation is restricted in the absence of sunlight, while wind availability shifts according to weather dynamics. Using only one renewable source can therefore cause power inconsistencies, diminished efficiency, and possible interruptions in service. Hybrid systems that merge solar and wind resources provide a reliable solution by enhancing energy continuity and operational stability.

   Another major concern is the lack of intelligent energy management. Without effective control techniques, renewable energy may not be fully harnessed, excess production could be wasted, and storage units might perform suboptimally. Intelligent energy flow mechanisms are essential for dynamically aligning production, storage, and demand. Advanced control methods improve power allocation, emphasize renewable consumption, and guarantee continuous EV charging with minimal dependence on conventional grid supply

4. PROPOSED SYSTEM

   The developed system is a renewable-based smart power management solution created to generate, store, track, and efficiently deploy electrical energy through an integrated control framework. It concentrates on sustainable solar generation combined with intelligent energy routing to enhance both reliability and operational efficiency.

   A 12V, 10W photovoltaic panel operates as the principal power source by transforming solar radiation into electricity. Because this output varies with environmental conditions, it is directed into a boost converter that raises the voltage to an optimal level for battery charging, thereby ensuring consistent storage.The elevated energy is retained within a 12V, 1.3Ah rechargeable battery, which functions as the main storage device and supplies power whenever solar production is insufficient. Embedded voltage and current sensors continuously observe battery activity, supporting safe functioning and longer service life by forwarding real-time data to the controller.At the heart of the system is a microcontroller responsible for intelligent decision-making. It aggregates sensor inputs, analyzes them through programmed logic, and accordingly manages charging operations, energy allocation, and system stability while optimizing load performance.An LCD panel connected to the controller provides instant visualization of parameters such as voltage, current, battery status, and operational conditions, improving usability and system clarity.For controlling loads, the design integrates an Electronic Speed Controller that receives commands from the microcontroller to regulate a Brushless DC motor. This arrangement promotes efficient motor behavior, reduces unnecessary power consumption, and ensures dependable operation based on available energy.

5. ARCHITECTURE
   1. METHODOLOGY

      The proposed solution is an advanced intelligent renewable energy management system engineered to generate, store, monitor, regulate, and efficiently distribute electrical power through a fully integrated control architecture. The design combines solar energy harvesting, real-time sensing mechanisms, battery-based energy storage, and microcontroller-driven decision-making to create a reliable and optimized power ecosystem. By prioritizing renewable energy utilization and implementing smart control strategies, the system enhances operational stability, reduces energy wastage, and supports sustainable power management.

   2. Solar Energy Generation and Power Conditioning

      A photovoltaic solar module rated at 12V and 10W functions as the primary energy conversion unit within the system. This panel captures solar irradiance and transforms it into usable electrical energy. However, because solar intensity varies with factors such as weather conditions, shading, and time of day, the generated voltage is inherently inconsistent. To address this variability, the solar output is routed through a boost converter that elevates and stabilizes the voltage to meet the required threshold for effective battery charging.The boost converter plays a critical role in maximizing energy harvesting

      by ensuring that even moderate solar output can be converted into a stable charging voltage. This conditioning process not only improves charging efficiency but also safeguards downstream components from voltage irregularities, thereby enhancing the durability and dependability of the entire system.

   3. Energy Storage with Intelligent Monitoring

      The conditioned electrical energy is stored in a 12V, 1.3Ah rechargeable battery, which serves as the central energy reservoir. This storage unit accumulates surplus energy during periods of strong solar exposure and provides a dependable backup supply when renewable generation is insufficent, such as during nighttime or unfavorable weather conditions.To maintain battery health and ensure safe operation, voltage and current sensors are tightly integrated with the storage unit. These sensors continuously track critical parameters, including voltage levels and charging or discharging currents, and transmit this data to the control unit in real time. Such monitoring enables the system to detect abnormal conditions early and implement protective measures against overcharging, deep discharge, overheating, and long-term capacity degradation. As a result, battery lifespan is extended while operational safety is significantly improved.

   4. Microcontroller-Based Intelligent Control Core

      At the heart of the architecture lies a microcontroller that functions as the central processing and decision-making entity. It aggregates input from all sensing devices and evaluates the data using predefined algorithms and logical thresholds. Based on this analysis, the microcontroller dynamically regulates battery charging cycles, balances energy flow, and maintains overall system equilibrium.The controller is also responsible for assessing power availability and distributing energy efficiently to connected loads. By continuously adapting to changing generation and consumption patterns, it ensures optimal utilization of available resources while preventing unnecessary power loss.To enhance system transparency, the microcontroller communicates key operational metrics including voltage magnitude, current flow, battery condition, and functional statusto an LCD interface. This real-time visualization enables users to monitor performance effortlessly, diagnose potential issues quickly, and maintain informed control over system operation.

   5. DCDC Conversion and Regulated Power Distribution

      In addition to the primary solar pathway, the system incorporates a DCDC converter to regulate electricity supplied either from auxiliary sources or directly from the battery. This converter ensures that the output voltage aligns precisely with the requirements of downstream components, thereby preventing mismatches that could compromise performance.Before distribution, the regulated output is verified using a voltage sensor, and the validated data is relayed to the microcontroller for supervisory control. This

      layered verification mechanism guarantees consistent voltage delivery, protects sensitive electronics, and improves overall conversion efficiency.

   6. Motor Control and Efficient Actuation

      For controlled mechanical operation, the system integrates an Electronic Speed Controller (ESC) that interfaces directly with the microcontroller. The ESC interprets control signals and adjusts the speed and torque of a Brushless DC (BLDC) motor accordingly.This configuration enables precise motor control while minimizing electrical losses and preventing overload conditions. The BLDC motor can support auxiliary mechanical processes or energy-related functions within the system, contributing to improved versatility. Its high efficiency and low maintenance requirements further align with the systems sustainability objectives.

   7. Intelligent Energy Flow Management

      A defining feature of the system is its capability for intelligent energy flow coordination. By continuously analyzing sensor inputs, the microcontroller orchestrates seamless interaction between energy generation units, storage components, and load demands. Renewable energy is prioritized whenever available, while battery resources are strategically utilized to maintain uninterrupted operation.This adaptive management approach allows the system to remain stable under fluctuating environmental conditions and varying load requirements. It optimizes battery usage, reduces dependence on external power sources, and enhances overall energy efficiency.

   8. Overall System Significance

   Through the integration of renewable generation, advanced monitoring, smart control algorithms, and regulated power conversion, the proposed intelligent energy management system delivers a robust, scalable, and sustainable solution for modern energy applications. Its ability to maintain voltage stability, protect storage components, and dynamically allocate power ensures high reliability and long-term operational effectiveness.

   Ultimately, this architecture represents a forward-looking approach to renewable energy utilization, promoting efficient resource management while supporting the broader transition toward environmentally responsible power systems.

   Fig 1 Architecture

   The Arduino UNO serves as the primary control platform, responsible for acquiring sensor data, executing programmed logic, and coordinating peripheral devices including motors, the LCD interface, and the buzzer. Its straightforward Embedded C programming environment, sufficient digital and analog pins, and strong acceptance in academic development make it a practical selection.Multiple infrared sensors are strategically positioned near the bins to identify object placement and determine the active disposal zone. These sensors transmit infrared radiation and detect reflected signals, allowing the controller to interpret status changes as digital HIGH or LOW inputs.A cylindrical inductive proximity sensor is integrated to recognize metallic materials. Its contactless operation ensures accurate detection while enhancing system durability.Since microcontrollers cannot directly supply the current required by motors, a motor driver module is incorporated to amplify current capacity and facilitate directional as well as switching control. This module governs DC motors responsible for mechanical bin positioning.The DC motors generate the motion necessary to rotate bins or operate flap mechanisms, enabling automatic routing of materials into the designated compartments.A 16×2 LCD module provides continuous visualization of operational parameters such as detected waste category, selected bin, and overall system condition, thereby improving usability during demonstrations.

   An onboard buzzer delivers audible notifications for detection events, bin capacity alerts, or fault conditions, strengthening user interaction.

   Power is delivered through an external battery pack that ensures adequate current for the entire system, particularly high-demand components like motors. Supporting voltage regulator modules maintain consistent output levels and safeguard electronics from electrical stress.

   Finally, connecting wires and a breadboard establish structured circuit pathways, promoting modularity, simplifying troubleshooting, and supporting efficient prototype development.

6. RESULTS

   Fig 2 Result

7. CONCLUSION

   The Hybrid Renewable EnergyDriven Intelligent Power System designed for EV charging infrastructure presents a progressive and environmentally responsible approach to meeting the escalating energy requirements of modern transportation. Through the seamless integration of renewable resources such as solar and wind energy with advanced control strategies and energy storage technologies, the system delivers a dependable, sustainable, and economically viable charging solution for electric vehicles.

   The implementation of intelligent power management enhances overall energy efficiency by ensuring optimal allocation and utilization of available resources. Simultaneously, it decreases reliance on traditional fossil fuel based electricity and alleviates pressure on existing power grids. This strategic framework plays a crucial role in lowering carbon footprints, improving energy performance, and accelerating the transition toward large-scale electric mobility adoption.Furthermore, the deployment of such innovative systems supports the development of environmentally conscious urban spaces, strengthens grid resilience, and promotes long-erm sustainability within both the transportation and energy sectors. Ultimately, this technology establishes a strong foundation for a cleaner, smarter, and more sustainable future.

8. FUTURE SCOPE

The proposed intelligent power management system based on renewable energy establishes a robust platform for achieving sustainable and efficient energy utilization. Despite its current capabilities, multiple avenues exist for future refinement and expansion that can further enhance system performance and adaptability.

A primary area for advancement involves incorporating additional renewable sources such as wind or bioenergy alongside the existing solar infrastructure. Developing a hybrid energy architecture would significantly strengthen system dependability by ensuring uninterrupted power generation even when environmental conditions fluctuate. This diversified energy mix would improve resilience and maintain consistent operational output.

Scalability represents another important direction for future development. By upgrading the capacity of photovoltaic arrays, expanding battery storage, and enhancing power electronic components, the system can be adapted to support electric vehicle charging requirements. The inclusion of fast- charging capabilities and high-density energy storage solutions would allow deployment in practical EV charging scenarios, including metropolitan regions and highway corridors.

Subsequent iterations of the system could also leverage advanced energy management methodologies powered by artificial intelligence and machine learning. Such technologies would enable predictive analysis of energy production, intelligent battery optimization, and automated charging schedules aligned with demand trends, meteorological forecasts, and grid accessibility. This data-driven approach would maximize efficiency while reducing operational uncertainty.

Another promising enhancement lies in integrating Internet of Things technology into the system architecture. IoT-enabled devices combined with cloud-based connectivity would facilitate continuous remote supervision, comprehensive data analysis, early fault identification, and predictive maintenance strategies. Additionally, system administrators and users could conveniently monitor performance metrics through dedicated mobile applications or web dashboards, thereby improving accessibility and operational transparency.

REFERNCE

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