Vijay R

@cvr.ac.in

Associate Professor and Electrical and Electronics Engineering
CVR College of Engineering, Hyderabad

Vijay R


                     Download Compact PDF  
https://researchid.co/vijaimtp

EDUCATION

He received his B.Eng. in Electrical & Electronics Engg. from Bannari Amman Institute of Technology, India.

M.Eng. in Power Systems Engg. from the Anna University of Technology, Coimbatore, India.

He had completed his Ph.D. in the Faculty of Electrical Engineering at Anna Univ, Chennai, India.

RESEARCH INTERESTS

AI in Power Systems
Optimization Algorithms
Smart Grid
Distributed Generation
19

Scopus Publications

374

Scholar Citations

10

Scholar h-index

11

Scholar i10-index

Scopus Publications

  • A multilayer data-driven framework for sustainable planning of electric vehicle charging infrastructure and distribution network expansion in smart cities
    Vijay Raviprabhakaran, Kotireddy Gopu, Phaninder Reddy Kolipaka, Aadi Tirumalesh
    Sustainable Energy Grids and Networks, 2026
  • Electric Vehicle Route Planning Using Time Frame with Decision Tree Algorithm
    Vijay Raviprabhakaran, Jesica Sarakonda, Vinay Udugula, Nishitha Varma Datla
    Journal of Transportation Engineering Part A Systems, 2026
    Electric vehicles are transforming urban mobility by providing a cleaner alternative to conventional combustion engines. However, challenges related to route optimization and access to reliable charging infrastructure continue to limit their performance in many developing countries. This study addresses these issues through an enhanced electric vehicle path planning with time frame (EVPPTF) approach that focuses on minimizing travel time and energy use while meeting user deadlines and accounting for the spatial distribution of charging stations. Although Dijkstra’s algorithm and decision tree models are well-established methods, the main contribution of the paper lies in the development of a hybrid and time-aware framework that integrates these techniques in a coordinated manner to support real-time path planning and charging decisions for electric vehicles. The proposed method improves the classical Dijkstra search by embedding energy consumption constraints, traffic-aware weights, and charging station accessibility checks. In addition, the decision tree model is extended to predict optimal charging stops using features derived from real-time data, including traffic flow, station availability, and remaining battery levels. The study also presents a comparative performance evaluation using real traffic patterns from Indian cities, which demonstrates that the hybrid method outperforms traditional techniques, including decision trees, random forests, k-nearest neighbors, and support vector regression. Validation in cities such as Hyderabad and Bengaluru shows that the proposed system consistently reduces travel duration and energy use under realistic conditions. The findings contribute to smarter and more sustainable urban transportation by supporting autonomous driving applications, guiding the placement of charging stations, and offering a scalable approach that can be adopted in developing countries facing similar EV mobility challenges.
  • Machine learning optimized green hydrogen fuel cell system for sustainable and efficient electric vehicle charging
    Vijay Raviprabhakaran, Parameshwari Pabbu
    Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy, 2026
    Green hydrogen has emerged as a transformative clean energy carrier with the potential to accelerate the global shift toward sustainable, low-carbon energy systems. It plays a crucial role in supporting the expansion of electric vehicles (EVs) by reducing greenhouse gas emissions and lowering the environmental impact of the transportation sector. In this study, hydrogen is generated through solar-powered electrolysis using a photovoltaic (PV) system integrated with a machine learning-based maximum power point tracking (MPPT) algorithm, which enhances solar energy harvesting and improves overall system efficiency. The research examines global trends in green hydrogen deployment with a particular focus on India, where rising electricity demand aligns with rapid EV infrastructure growth. Two operating configurations are examined in this study, namely a standalone EV charging station without ML optimization and a grid-connected system that incorporates ML-driven MPPT control. Simulations were performed for hydrogen flow rates of 10, 14, 45, and 80 cubic meters per second using real-time solar data from several Indian states to assess system performance under practical environmental conditions. The standalone proton exchange membrane (PEM) fuel cell achieves an efficiency of about 50%. In comparison, the ML-optimized grid-connected system reaches 60.43%, demonstrating the value of intelligent energy management in improving renewable hydrogen utilization. This study introduces a novel ML-enhanced PV hydrogen PEM fuel cell architecture that integrates solar-powered electrolysis, data-driven MPPT control, hydrogen storage, and fuel cell-based power conversion. It also provides a comparative analysis of standalone and grid-connected operations using real multi-state solar data to reveal the influence of hydrogen flow rates on system performance. Overall, the proposed framework offers a sustainable and scalable solution for developing regions facing rising energy demands and highlights the powerful synergy between renewable hydrogen, artificial intelligence, and EV infrastructure in creating clean, resilient, and future-ready energy systems.
  • Integrated fleet sizing and routing optimization for shared electric vehicles under energy and charging constraints
    Vijay Raviprabhakaran
    Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, 2026
    Rapid urbanization and the growing demand for personal mobility have intensified traffic congestion, energy consumption, and environmental stress in metropolitan regions. Shared electric mobility offers a viable pathway toward sustainable urban transportation. However, large-scale adoption remains constrained by interrelated challenges, including fleet sizing, routing decisions, battery limitations, and the availability of charging infrastructure. Addressing these complexities requires advanced optimization approaches capable of managing nonlinear system dynamics, uncertain travel demand, and time-varying energy states. This paper presents a hybrid optimization framework that integrates Ant Colony Optimization, Bacterial Swarm Optimization, and Deep Reinforcement Learning (ACO–BSO–DRL) to jointly address fleet sizing, routing, and energy management in shared electric vehicle systems. Urban transportation networks are modeled as constrained graphs that capture the evolution of battery state of charge, charging and battery replacement decisions, and service deadline requirements. ACO supports efficient global route exploration, BSO enhances local solution refinement, and DRL adaptively updates decision policies and algorithm parameters in response to evolving system conditions. The framework is evaluated through extensive simulation studies based on realistic urban scenarios in Hyderabad and Bengaluru, India. A comparative analysis of standalone metaheuristic and learning-based methods demonstrates consistent reductions in required fleet size, improved energy utilization efficiency, and faster, more stable convergence. Multi-run statistical evaluation under randomized initial conditions further confirms the robustness and repeatability of the proposed approach. Overall, the results demonstrate that intelligent hybrid optimization has significant potential to enhance the efficiency and sustainability of shared electric mobility systems by reducing unnecessary vehicle deployment, minimizing battery replacements, and improving charging coordination. This work aligns with Sustainable Development Goals (SDG) 11 (Sustainable Cities and Communities), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action) by promoting energy-efficient, low-emission, and resilient urban transportation. Future extensions may incorporate real-time traffic data, renewable energy-powered charging infrastructure, and multi-modal mobility integration to enhance practical applicability.
  • Electric automobile route scheduling with time setting using machine learning methods
    Vijay Raviprabhakaran, Jesica Sarakonda, Vinay Udugula, Nishitha Varma Datla
    International Journal of Sustainable Transportation, 2026
  • Automated Protection Mechanism for Transformer Overloading with a Voiceover Alert System
    Vijay Raviprabhakaran, Aparna Ayyagari, Surender Reddy Salkuti
    Green Energy and Technology, 2025
  • Plastic Material Identification and Categorization by Applying Convolutional Neural Network
    Vijay Raviprabhakaran
    2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation Sefet 2024, 2024
    Plastic litter is the foremost ecological problem of our era. The local distribution of plastic items is currently poorly segregated, which hinders efforts to reduce negative effects and create reform plans. The processing of plastic garbage is indeed a worldwide concern. Researchers have developed automated management approaches that improve the efficiency of reprocessing since manual garbage disposal is a difficult and costly operation. Plastic trash from the garbage disposal conveyor can be identified independently using artificial intelligence (AI), especially deep learning and image processing algorithms. Large categories of materials containing paper, plastics, metals, and glasses are all subject to the same waste management methods and techniques. Sorting from the countless point breeds of esoteric the set, for the event, to a single countless glass or plastic method is the hardest problem, though. Later ideas for the polymer's reprocessing polyethylene terephthalate (PET) can be turned into polyester material, which is important. Thus, they must restrict how these wastes are isolated by using convolutional neural networks (CNN), and in-depth drills are a great option. Plastic products, specifically PET, polypropylene, and polystyrene, are the greatest fountain of household waste. The primary issue criticized in this article is the literature study and advancement of computerization progression techniques for plastic compost waste such as PET, polypropylene (PP), high-density polyethylene (HDPE), and low-density polyethylene (LDPE), which are then activated in either a riddle plant or a resident's house. Furthermore, these plastic segregations can project on your mobile devices the type of waste consumed in your house, which is useful to municipal garbage collectors.
  • Enriched Power Yield from Photovoltaic Systems Under Partial Shadowing Conditions by Velocity Grey Wolf Algorithm
    Vijay Raviprabhakaran, Sai Kiran Gajwari, Andrews Gunturu, Nikitha Tadkale
    2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation Sefet 2024, 2024
    Photovoltaic (PV) systems face a major problem while operating in partial shadowing situations. This is because several crests in the PV array's voltage-current characteristic can result in inefficient power extraction. Under such circumstances, standard Maximum Power Point Tracking (MPPT) methods are unable to precisely trace the Global Maximum Power Point (GMPP). The paper proposes the utilization of the Velocity Grey Wolf Algorithm (VGWA) to address this challenge. VGWA encouraged through the trailing behaviours of grey wolves, offers the potential to effectively optimize the PV arrangement's functioning by adaptively modifying the tracking process in response to varying ecological situations, including partial shadowing. Through experiments and simulations, the efficacy of VGWA in accurately tracing the GMPP, even in complex shading scenarios, was developed with enhanced efficiency and fleeter conjunction compared to traditional MPPT methods. By studying partial shade conditions, this research advances MPPT performance for PV systems, especially in difficult locations.
  • Household Power Consumption Analysis using Machine Learning
    Vijay Raviprabhakaran, Pusuluri Pranay, Bhavana Nendralla, Lakkepuram Shiva Pranay
    2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation Sefet 2024, 2024
    To understand the complex relationships between the power consumption of various household appliances and the overall power usage, this essay delves into the field of machine learning. The principal aim is to develop prognostic models that can estimate the overall active power consumption by analyzing the energy consumption of designated household spaces, such as the kitchen, laundry room, air conditioning unit, and electric water heater. Through meticulous analysis and implementation of machine learning techniques, insight into the fundamental connections that underpin patterns of energy consumption is shed. The implications of the findings extend beyond mere predictive accuracy, offering invaluable insights for optimizing energy usage and informing future power management strategies. This study emphasizes how important it is to apply machine learning to interpret trends in household power consumption to make informed decisions and promote sustainable power use. Momentarily, household power consumption monitoring utilizing the XG Boost algorithm will make use of sophisticated data analytics to maximize energy use, improve efficiency, and give customers individualized advice on sustainable living habits.
  • Automated Revealing and Warning System for Pits and Blockades on Roads to Assist Carters
    Vijay Raviprabhakaran, Prasanth Dharavathu, Dhanush Adithya Gopaluni, Abhinav Reddy Jale
    Lecture Notes in Electrical Engineering, 2024
  • Economical Modelling and Manufacturing of a Prosthetic ARM
    Vijay Raviprabhakaran
    Wireless Personal Communications, 2023
  • Quorum sensing centered bacterial horde algorithm for global optimization
    Vijay Raviprabhakaran
    Concurrency and Computation Practice and Experience, 2023
  • Clonal Assortment Optimization Procedure to Unravel Cost-Effective Power Dispatch Problem
    Vijay Raviprabhakaran
    Lecture Notes in Electrical Engineering, 2023
  • Performance enrichment in optimal location and sizing of wind and solar PV centered distributed generation by communal spider optimization algorithm
    Vijay Raviprabhakaran
    COMPEL the International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 2022
  • Optimal Scheme and Power Controlling aspects in Shipboard System
    Vijay Raviprabhakaran, Teja Sree Mummadi
    Lecture Notes in Electrical Engineering, 2020
  • Quorum Sensing Driven Bacterial Swarm Optimization to Solve Practical Dynamic Power Ecological Emission Economic Dispatch
    Raviprabakaran Vijay
    International Journal of Computational Methods, 2018
  • Enhanced ant colony optimization to solve the optimal power flow with ecological emission
    Vijay Raviprabakaran, Ravichandran Coimbatore Subramanian
    International Journal of System Assurance Engineering and Management, 2018
  • Scheduling practical generating system using an improved bacterial swarm optimization
    R Vijay, C. Ravichandran
    Tehnicki Vjesnik, 2016
  • Enriched Biogeography-Based optimization algorithm to solve economic power dispatch problem
    Vijay Raviprabhakaran, Coimbatore Subramanian Ravichandran
    Advances in Intelligent Systems and Computing, 2016

RECENT SCHOLAR PUBLICATIONS

  • Electric Vehicle Route Planning Using Time Frame with Decision Tree Algorithm
    V Raviprabhakaran, J Sarakonda, V Udugula, NV Datla
    Journal of Transportation Engineering, Part A: Systems 152 (6), 06026001 , 2026
    2026
    Citations: 1
  • A Comprehensive Analytical Review of Renewable Energy-Based Electrolyzer Technologies for Sustainable Green Hydrogen Production
    V Raviprabhakaran, MB Bipilli, P Kondapalli Naga Bhanu, V Mara
    International Journal of Sciences and Innovation Engineering 3 (5), 143-154 , 2026
    2026
  • Geothermal-based integrated systems for hydrogen production - A Review
    V Raviprabhakaran, BS Rithvik, T Sai Ganesh, RK Haindavi, J Purmi
    International Journal of Sciences and Innovation Engineering 3 (4), 2974-2982 , 2026
    2026
  • A Review of Autonomous Vehicle Fleets in Smart Cities
    V Raviprabhakaran, T Jain, H Gundu, B Harshita
    International Journal of Sciences and Innovation Engineering 3 (5), 26-34 , 2026
    2026
  • A multilayer data-driven framework for sustainable planning of electric vehicle charging infrastructure and distribution network expansion in smart cities
    V Raviprabhakaran, K Gopu, PR Kolipaka, A Tirumalesh
    Sustainable Energy, Grids and Networks 46, 102183 , 2026
    2026
    Citations: 1
  • Integrated fleet sizing and routing optimization for shared electric vehicles under energy and charging constraints
    V Raviprabhakaran
    Proceedings of the Institution of Mechanical Engineers, Part D: Journal of … , 2026
    2026
    Citations: 1
  • Machine learning optimized green hydrogen fuel cell system for sustainable and efficient electric vehicle charging
    V Raviprabhakaran, P Pabbu
    Proceedings of the Institution of Mechanical Engineers, Part A: Journal of … , 2025
    2025
    Citations: 3
  • Electric automobile route scheduling with time setting using machine learning methods
    V Raviprabhakaran, J Sarakonda, V Udugula, NV Datla
    International Journal of Sustainable Transportation 20 (4), 1-16 , 2025
    2025
    Citations: 5
  • Automated Protection Mechanism for Transformer Overloading with a Voiceover Alert System
    V Raviprabhakaran, A Ayyagari, SR Salkuti
    Next-Generation Green Energy Technologies for Sustainable Development, 293-305 , 2025
    2025
    Citations: 1
  • An Analytical Study of Fuel Cell Technologies for Green Energy Generation
    V Raviprabhakaran, P Pabbu
    Asian Journal of Electrical Sciences 14 (1), 15–22 , 2025
    2025
    Citations: 1
  • Enriched Power Yield from Photovoltaic Systems Under Partial Shadowing Conditions by Velocity Grey Wolf Algorithm
    R Vijay, G Sai Kiran, G Andrews, T Nikitha
    2024 IEEE 4th International Conference on Sustainable Energy and Future … , 2024
    2024
    Citations: 1
  • Household Power Consumption Analysis using Machine Learning
    R Vijay, P Pusuluri, N Bhavana, SP Lakkepuram
    2024 IEEE 4th International Conference on Sustainable Energy and Future … , 2024
    2024
    Citations: 12
  • Plastic Material Identification and Categorization by Applying Convolutional Neural Network
    V Raviprabhakaran
    2024 IEEE 4th International Conference on Sustainable Energy and Future … , 2024
    2024
    Citations: 6
  • PROSTHETIC ARM
    V Raviprabhakaran
    IN Patent App. 202441053283 A , 2024
    2024
  • Economical Modelling and Manufacturing of a Prosthetic ARM
    V Raviprabhakaran
    Wireless Personal Communications 130 (3), 1819-1832 , 2023
    2023
    Citations: 6
  • Clonal Assortment Optimization Procedure to Unravel Cost-Effective Power Dispatch Problem
    V Raviprabhakaran
    Soft Computing Applications in Modern Power and Energy Systems 975, 39-53 , 2023
    2023
    Citations: 10
  • Quorum sensing centered bacterial horde algorithm for global optimization
    V Raviprabhakaran
    Concurrency and Computation: Practice and Experience 35 (8), e7627 , 2023
    2023
    Citations: 13
  • Automated Revealing and Warning System for Pits and Blockades on Roads to Assist Carters
    V Raviprabhakaran, P Dharavathu, DA Gopaluni, AR Jale
    International Conference on Computational Intelligence in Machine Learning … , 2022
    2022
    Citations: 1
  • Maximizing Power Yield from Mismatched Environment in Grid-Connected PV System by Fuzzy Logic Control
    M Krishnaprasad, V Raviprabhakaran
    CVR Journal of Science and Technology 22 (1), 63-69 , 2022
    2022
    Citations: 1
  • Performance enrichment in optimal location and sizing of wind and solar PV centered distributed generation by communal spider optimization algorithm
    V Raviprabhakaran
    COMPEL-The international journal for computation and mathematics in … , 2022
    2022
    Citations: 15

MOST CITED SCHOLAR PUBLICATIONS

  • Enhanced ant colony optimization to solve the optimal power flow with ecological emission
    V Raviprabakaran, RC Subramanian
    International Journal of System Assurance Engineering and Management 9 (1 … , 2018
    2018
    Citations: 74
  • Intelligent bacterial foraging optimization technique to economic load dispatch problem
    R Vijay
    International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231 … , 2012
    2012
    Citations: 59
  • Quorum Sensing Driven Bacterial Swarm Optimization to Solve Practical Dynamic Power Ecological Emission Economic Dispatch
    R Vijay
    International Journal of Computational Methods 15 (3), 1850089-24 , 2018
    2018
    Citations: 31
  • Enriched biogeography-based optimization algorithm to solve economic power dispatch problem
    R Vijay, CS Ravichandran
    Proceedings of Fifth International Conference on Soft Computing for Problem … , 2016
    2016
    Citations: 18
  • OPTIMAL AND STABLE OPERATION OF MICROGRID USING ENRICHED BIOGEOGRAPHY BASED OPTIMIZATION ALGORITHM
    V Raviprabakaran
    Journal of Electrical Engineering 17 (4), 1-11 , 2017
    2017
    Citations: 16
  • Performance enrichment in optimal location and sizing of wind and solar PV centered distributed generation by communal spider optimization algorithm
    V Raviprabhakaran
    COMPEL-The international journal for computation and mathematics in … , 2022
    2022
    Citations: 15
  • Scheduling Practical Generating System Using an Improved Bacterial Swarm Optimization
    R Vijay, R C. Subramanian
    Tehnički vjesnik 23 (5), 1307-1315 , 2016
    2016
    Citations: 15
  • Quorum sensing centered bacterial horde algorithm for global optimization
    V Raviprabhakaran
    Concurrency and Computation: Practice and Experience 35 (8), e7627 , 2023
    2023
    Citations: 13
  • Household Power Consumption Analysis using Machine Learning
    R Vijay, P Pusuluri, N Bhavana, SP Lakkepuram
    2024 IEEE 4th International Conference on Sustainable Energy and Future … , 2024
    2024
    Citations: 12
  • Optimal Placement and Sizing of Distributed Power Sources in Microgrid for Power Loss Minimization Using Bat Motivated Optimization Algorithm
    R Vijay, CS Ravichandran
    Asian Journal of Research in Social Sciences and Humanities 6 (8), 252-266 , 2016
    2016
    Citations: 12
  • Clonal Assortment Optimization Procedure to Unravel Cost-Effective Power Dispatch Problem
    V Raviprabhakaran
    Soft Computing Applications in Modern Power and Energy Systems 975, 39-53 , 2023
    2023
    Citations: 10
  • Anti-Islanding Protection of Distributed Generation Based on Social Spider Optimization
    R Vijay, V Priya
    International Journal of Advanced Engineering Research and Science 4 (6), 32-40 , 2017
    2017
    Citations: 9
  • Optimal Sitting of PV-Wind-Energy Storage System Integrated Micro Grid Using Artificial Bee Colony Optimization Technique
    R Vijay, Sowmya, Ramachandradurai
    International Journal of Innovative Research in Computer and Communication … , 2017
    2017
    Citations: 9
  • Elephant Herding Optimization for Optimum Allocation of Electrical Distributed Generation on Distributed Power Networks
    R Vijay, M Abhilash
    Asian Journal of Electrical Sciences 7 (2), 70-76 , 2018
    2018
    Citations: 8
  • A detailed investigation on conventional and meta-heuristic optimization algorithms for economic power scheduling problems
    R Vijay, CS Ravichandran
    International Journal of Engineering Trends and Applications 3 (4), 40-53 , 2016
    2016
    Citations: 8
  • Optimal Placement and Sizing of Solar Constructed DG Using SSO Technique
    R Vijay, R Antrut Jaffrin, CS Ravichandran
    International Journal of Computer Science Trends and Technology 4 (3), 333-342 , 2016
    2016
    Citations: 7
  • Plastic Material Identification and Categorization by Applying Convolutional Neural Network
    V Raviprabhakaran
    2024 IEEE 4th International Conference on Sustainable Energy and Future … , 2024
    2024
    Citations: 6
  • Economical Modelling and Manufacturing of a Prosthetic ARM
    V Raviprabhakaran
    Wireless Personal Communications 130 (3), 1819-1832 , 2023
    2023
    Citations: 6
  • Optimal Scheme and Power Controlling aspects in Shipboard System
    V Raviprabhakaran, TS Mummadi
    Innovations in Electrical and Electronics Engineering 626, 367-379 , 2020
    2020
    Citations: 6
  • Electric automobile route scheduling with time setting using machine learning methods
    V Raviprabhakaran, J Sarakonda, V Udugula, NV Datla
    International Journal of Sustainable Transportation 20 (4), 1-16 , 2025
    2025
    Citations: 5

GRANT DETAILS

Published a Copyright for the topic “Quorum Sensing Based Bacterial Swarm Algorithm to Solve Economic Power Dispatch Problem” from Copyright Office – Government of India on 14.10.2019 (Registration No: L-86320/2019)

SOCIAL, ECONOMIC, or ACADEMIC BENEFITS

Currently, I am running a YouTube Channel named "Power Systemz" In which I have covered certain topics in the Power system operation & Control, Power system generation, and Economic operation of Power systems topics in a lucid manner that is being watched by students and the research community. On this YouTube Channel, I got more than 36.2K Views & 4.2K plus watch Hours and still more. I have planned to communicate my video lectures on YouTube to my viewers and students’ community about the latest technologies shortly.