Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Teja, Srungaram Ravi
Unknown Affiliation
Control & Systems Engineering Electrical & Electronics Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Machine learning based optimal control of modular converter for PV assisted power supply systems Teja, Srungaram Ravi; Yadlapati, Kishore
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2570-2579

Abstract

This paper presents the topology and machine learning-based intelligent control of a single-stage grid-connected high-power photovoltaic (PV) system for quality power export to the grid and optimal net energy utilization. A nineteen-level bi-modular inverter is proposed for efficient single-stage PV power conversion. The proposed integrated intelligent machine learning-based control serves for power conversion control as well as supervisory control for hourly PV energy estimation and load demand control for optimal energy consumption. The objectives of power control are extracting maximum power from PV sources and exporting power to the grid at unity power factor. While the objectives for supervisory control are local load demand control for exporting power at higher export prices. The proposed system is implemented using MATLAB/Simulink to validate the efficiency of power conversion, effectiveness of machine learning for energy estimation, and load relay control for optimal energy pricing. The results proved efficient tracking of maximum power, unity power factor at grid terminals, and load relay control for PV energy availability and export cost function.
Development of dual functional converter for drive and charging power conversion for EV drive Tadivaka, Teja Sreenu; Kumar, Malligunta Kiran; Teja, Srungaram Ravi; Reddy, Ch. Rami
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i2.pp794-807

Abstract

The adaptability of electric vehicle drives is primarily concerned with the size and efficiency of power conversion. This paper presents a unified power converter for the drive and charge functions of brushless direct current-based electric vehicle drives (BLDC). The symmetrical utilization of BLDC phase windings during charging operation is implemented for efficient power conversion. The unified converter operation, configuration, and control are presented. The proposed converter is simulated in the MATLAB/Simulink platform. The performance is evaluated using operational variables such as voltage, current, torque, and speed. A comparative study is presented regarding the size and efficiency of the proposed and existing drives. The proposed drive achieved 0.01 p.u. ripple in torque, 10-sec transient time for a change in speed full throttle command, and unity power factor current for charging operation, proving its robustness over the comparable drives.
Enhancing urban EV integration: a data-driven hybrid approach to charging station optimization and energy management Hussain, Shaik Mohammed; Swapna, Ganapaneni; Rao, Kambhampati Venkata Govardhan; Kumar, Malligunta Kiran; Teja, Srungaram Ravi
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.
Smart charging of electric vehicles at a charging station using machine learning and pressure pad energy harvesting Tadi, Kumara Swamy; Swapna, Ganapaneni; Rao, Kambhampati Venkata Govardhan; Kumar, Malligunta Kiran; Teja, Srungaram Ravi
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.
Co-Authors Hussain, Shaik Mohammed Malligunta Kiran Kumar Rao, Kambhampati Venkata Govardhan Reddy, Ch. Rami Swapna, Ganapaneni Tadi, Kumara Swamy Tadivaka, Teja Sreenu Yadlapati, Kishore