Article Per Year (5 Year)
p-Index From 2021 - 2026
1.456
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Malligunta Kiran Kumar
Koneru Lakshmaiah Education Foundation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Smart charging of electric vehicles at a charging station using machine learning and pressure pad energy harvesting Kumara Swamy Tadi; Ganapaneni Swapna; Kambhampati Venkata Govardhan Rao; Malligunta Kiran Kumar; Srungaram Ravi Teja
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10635

Abstract

The rapid growth of electric vehicles (EVs) demands intelligent, cost-effective, and sustainable charging solutions. This paper introduces a smart EV charging station system that integrates machine learning (ML) with pressure pad–based energy harvesting. The system forecasts energy demand, predicts vehicle types and slot needs, and recommends optimal charging times using real-time data such as state of charge (SoC), battery health, and user behavior patterns. ML models such as long short-term memory (LSTM) and random forest are employed to ensure accurate scheduling and forecasting. A smart display, the display slot indicator (DSI), powered by sensors and station data, guides users with live cost, time, and slot availability, including alternate suggestions during peak demand. The pressure pad not only contributes to energy recovery but also aids in real-time vehicle detection and traffic regulation within the station. With scalable capacity and intelligent automation, this system can support more than 400 EVs per day, minimizing operational load and energy waste while maximizing convenience and sustainability.
Optimized control approach for bidirectional wireless power transfer systems with vehicle-to-grid integration Mareedu Hari Venkatesh; Malligunta Kiran Kumar; Chandra Babu Guttikonda; Thalanki Venkata Sai Kalyani; Kambhampati Venkata Govardhan Rao
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10740

Abstract

The transition to electric vehicles (EVs) has intensified the need for efficient vehicle-to-grid (V2G) and grid-to-vehicle (G2V) systems. Bidirectional wireless power transfer (BWPT) presents a seamless and intelligent approach to energy exchange, particularly under dynamic tariff and grid demand conditions. This study aims to model and simulate a Python-based rule-driven BWPT system to evaluate energy efficiency and economic performance in V2G/G2V applications. A synthetic dataset representing grid demand and time-of-use (TOU) pricing over seven days was used to simulate real-world operating conditions. The model incorporates state-of-charge (SoC) dynamics, bidirectional power control logic, and profit calculation using a 15-minute resolution over 672 time steps. The simulation achieved a total energy exchange of 122.8 kWh and a cumulative net profit of ?536.67, with daily profits averaging ?76.6. SoC levels were effectively maintained between 20% and 90%, and power flows adapted accurately to tariff variations. The study confirms the feasibility of a lightweight, reproducible BWPT model capable of delivering optimized energy management and economic returns. The simulation approach offers strong potential for academic, research, and pre-deployment evaluation of intelligent charging systems.
Advanced artificial intelligence-based multi-sensor fusion for environmental perception in autonomous electric vehicles Billu Naveen; Malligunta Kiran Kumar; Thalanki Venkata Sai Kalyani; Thulasi Bikku; Kambhampati Venkata Govardhan Rao
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10739

Abstract

As autonomous electric vehicles (AEVs) continue to evolve, the demand for robust obstacle detection systems becomes increasingly critical to ensure safety, efficiency, and adaptability in real-world environments. This review presents a comprehensive synthesis of recent advancements in sensor fusion technologies, emphasizing the integration of light detection and ranging(LiDAR), radar, and camera-based vision systems. It highlights the role of deep learning architectures—such as you only look once (YOLO), convolutional neural networks (CNNs), and related neural models—in enhancing object detection, classification, and segmentation. The review categorizes key research themes, including fusion methodologies, real-time processing, edge computing, performance in adverse weather conditions, pedestrian detection, and sensor calibration. Special attention is paid to techniques that merge spatial, velocity, and semantic data to mitigate individual sensor limitations. The paper also discusses hardware-accelerated solutions for low-latency inference and the use of lightweight models for deployment on edge devices. Benchmark datasets, of vehicle-to-everything (V2X) and internet of thing (IoT)-based infrastructure, and calibration challenges are examined for their roles in ensuring accuracy and reliability. Drawing from over 100 referenced studies, this work serves as a foundational resource for researchers and developers aiming to advance artificial intelligence (AI)-based sensor fusion systems in next-generation AEVs.
Enhancing urban EV integration: a data-driven hybrid approach to charging station optimization and energy management Shaik Mohammed Hussain; Ganapaneni Swapna; Kambhampati Venkata Govardhan Rao; Malligunta Kiran Kumar; Srungaram Ravi Teja
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10614

Abstract

Electric vehicles (EVs) are pivotal to sustainable urban mobility, but their large-scale adoption in developing cities depends on efficient charging infrastructure and grid stability. This study proposes a hybrid deep learning framework to optimize EV charging station placement and energy scheduling in Vijayawada, India, projected to host 70,000 EVs by 2028. A convolutional neural network (CNN) is employed to classify charger types (Fast vs. Level 2) based on spatial features such as geospatial coordinates, population density, and traffic volume, while a long short-term memory (LSTM) network forecasts hourly charging demand using synthetic 24-hour sequences. The dataset comprises 108 candidate locations, designed to mirror real usage patterns. Model performance is evaluated using classification accuracy and mean absolute error (MAE). Results indicate that the CNN achieved 92% accuracy in charger type prediction, while the LSTM produced an hourly demand forecast with an MAE of 25 sessions/hour. These outcomes demonstrate the framework’s ability to reduce grid stress by shifting peak loads and strategically placing chargers in high-demand zones. The study provides a scalable and adaptable solution for EV infrastructure planning, enabling resilient grid integration, and supporting sustainable urban energy systems.
Co-Authors Ashok Kumar Kolluru B. V. Rajanna Batchalakuri Jyothi Beeravalli Mounika Bhavana Pabbuleti Billu Naveen Bommasani Ganesh Babu Chandra Babu Guttikonda Dungrothu Kalpana G. Anjali Devi Ganapaneni Swapna Hrushitha Kalapala Jasti Venkata Ramesh Babu K. V. Govardhan Rao Kambhampati Venkata Govardhan Rao Kambhampati Venkata Govardhan Rao Kumara Swamy Tadi L. Sirisaiah Lokesh Kotha Mareedu Hari Venkatesh Meda Uma Santhosh Chandra Motaparthi Nagaraju Mudundi Rekha Pidikiti Tripura Rami Reddy Chilakala Sanaboina Sai Srilakshmi Shaik Mohammed Hussain Srungaram Ravi Teja Subhash Manchikanti Swapna Ganapaneni T. Rakesh Thalanki Venkata Sai Kalyani Thulasi Bikku