Article Per Year (5 Year)
p-Index From 2021 - 2026
1.048
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE) Bulletin of Electrical Engineering and Informatics
Ahammad, Shaik Hasane
Unknown Affiliation
Control & Systems Engineering Electrical & Electronics Engineering

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Design and development of AC motor speed controlling system using touch screen with over heat protection Rani, Prathipati Ratna Sudha; Eragamreddy, Gouthami; Inthiyaz, Syed; Ravikanth, Sivangi; Najumunnisa, Mohammad; Rajanna, Bodapati Venkata; Kumar, Cheeli Ashok; Ahammad, Shaik Hasane
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2429-2440

Abstract

Design and implementation of an AC motor speed control and monitoring system based on a touch screen interface with built-in overheat protection, utilizing Arduino, meets the increasing demand for efficient, user-friendly motor control in many industrial applications. This system offers an easy-to-use interface to manage the speed of an AC motor, with real-time feedback and adjustments through a touch screen display. The system employs an Arduino microcontroller, which accepts inputs from the touch screen and processes these to regulate the motor's speed through a pulse width modulation (PWM) method. The system also has an overheat protection system, which it is able to monitor the temperature of the motor via a temperature sensor. When the motor reaches a predetermined temperature, the system automatically shuts off power to avoid damage. The intuitive touch screen facilitates convenient monitoring of motor parameters like temperature, giving a smooth experience to operators. The modular design of the system provides scalability across applications, ranging from household appliances to large industrial systems, with reliability, energy efficiency, and safety in motor-driven processes.
Bidirectional power converter for electrical vehicle with battery charging and smart battery management system Rajanna, Bodapati Venkata; Krishnaiah, Kondragunta Rama; Reddy, Ganta Raghotham; Ahammad, Shaik Hasane; Najumunnisa, Mohammad; Inthiyaz, Syed; Eragamreddy, Gouthami; Sudhakar, Ambarapu; Kolukula, Nitalaksheswara Rao
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2592-2604

Abstract

In electric vehicles (EVs), efficient energy management is critical for reliable power transfer between the battery and motor. This paper presents the design and implementation of a bidirectional DC-DC converter equipped with a smart battery management system (BMS). The system supports bidirectional power flow, operating in boost mode during acceleration and buck mode during regenerative braking, thereby enhancing overall energy efficiency and vehicle performance. A PIC microcontroller governs the system, performing real-time monitoring of key battery parameters such as state of charge (SOC), state of health (SOH), voltage, and temperature. Safety features include automatic cooling fan activation when the temperature exceeds 45 °C and generator startup when battery voltage falls below 23 V. Real-time data is displayed via an LCD interface to improve user interaction and system transparency. The proposed system achieved a conversion efficiency of 90-93% during experimental testing, with stable switching, reliable automation, and effective thermal protection. The embedded energy management system optimizes charging and discharging cycles while preventing overcharging, deep discharge, and thermal stress. This intelligent, automated power converter enhances battery life, improves EV reliability, and contributes to sustainable transportation by enabling features like vehicle-to-grid (V2G) energy transfer. The proposed architecture is well-suited for integration into modern EV infrastructure. Although the system architecture supports future V2G integration, V2G functionality was not implemented or tested in the present experimental setup.
A high-efficiency transformerless buck-boost inverter with fuzzy logic control for grid-connected solar PV systems Venkata Rajanna, Bodapati; Rama Krishnaiah, Kondragunta; Ramaiah, Veerlapati; Ahammad, Shaik Hasane; Najumunnisa, Mohammad; Inthiyaz, Syed; Rao Kolukula, Nitalaksheswara; Sudhakar, Ambarapu
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10752

Abstract

Transformerless inverters are increasingly favored in grid-connected photovoltaic (PV) systems due to their higher efficiency, reduced size, and lower cost. This paper presents a novel transformerless inverter topology that integrates buck boost conversion with an advanced fuzzy logic controller (FLC) to enhance energy extraction and power quality under dynamically changing solar conditions. The proposed system employs a sine triangle pulse width modulation (PWM) scheme in conjunction with the FLC to improve waveform quality and system responsiveness. By dynamically adapting to variations in irradiance and load, the control strategy reduces the total harmonic distortion (THD) from 36.51% to 1.51%, significantly enhancing compliance with international grid standards. Additionally, a novel grounding technique is implemented to mitigate common mode leakage currents, a typical issue in transformerless systems, without the need for galvanic isolation. Comprehensive MATLAB/Simulink simulations validate the inverter’s performance, demonstrating superior dynamic behavior, harmonic suppression, and overall reliability. The proposed architecture offers a compact, cost effective, and high performance solution for next generation grid integrated solar PV systems.
Brain tumor classification using PCA-NGIST features with an enhanced RELM classifier Babu, Bukkapatnam Rakesh; Rajesh, Vullanki; Rajanna, Bodapati Venkata; Ahammad, Shaik Hasane
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10742

Abstract

Brain tumours may cause severe health risks because of abnormal cell growth, which may result in organ malfunctions and death in adulthood. As precise identification of the tumour type is required for effective treatment. Magnetic resonance imaging (MRI) has recently been provided as an effective method for brain tumour diagnosis by computer-based based systems. To categorize brain tumours from MRI images, the paper offered a fusion model integrating an enhanced regularized extreme learning machine (RELM) classifier with principal component analysis (PCA) and normalized GIST (NGIST) feature extraction. While NGIST extracts strong spatial and texture features essential for modelling the tumour, PCA reduces the dimension of the input features without sacrificing significant data patterns. The improved RELM efficiently categorizes brain tumours into three categories: pituitary, meningioma, and glioma. It is optimized to improve learning capacity and generalization. The novelty of this study lies in the integration of NGIST descriptors with PCA-driven dimensionality reduction and an enhanced RELM classifier in a single lightweight framework. Unlike conventional methods that trade accuracy for computational cost, the proposed model ensures high precision and recall while remaining computationally efficient. This unique fusion demonstrates significant improvements in both diagnostic accuracy of 96% and clinical applicability, offering a balanced solution for real-time brain tumor classification.
Analysis of different converter topologies for EV applications Rajanna, Bodapati Venkata; Krishnaiah, Kondragunta Rama; Girija, Sakimalla Prabhakar; Ahammad, Shaik Hasane; Najumunnisa, Mohammad; Inthiyaz, Syed; Eragamreddy, Gouthami; Ambati, Giriprasad; Kolukula, Nitalaksheswara Rao
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp518-532

Abstract

Electric vehicles (EVs) are gaining global prominence due to their high efficiency, low noise, and minimal carbon emissions. A critical aspect of EV performance lies in the interaction between energy storage systems (ESS) and power converters. Nonetheless, power delivery from storage units tends to be unreliable and needs strong converter units for effective and stable energy transmission. Several forms of direct current-to-direct current conversion systems used in electric vehicles are thoroughly examined in the paper, including both isolated and non-isolated designs such as those with the cuk, flyback, and push-pull architectures. The paper looks at converter categorization, control methods such as proportional-integral and artificial neural networks, as well as the method of modulation using unipolar and bipolar sinusoidal pulse-width modulation (PWM). Additionally, the role of optimization algorithms in improving converter performance is explored. Simulations were conducted using MATLAB/Simulink to evaluate each topology under varying load and input voltage conditions. The results demonstrate that the Push-Pull converter has the best efficiency for high-power applications, while the Cuk and Flyback converters are best for applications requiring continuous current and low-power, compact designs, respectively. This research offers insights for choosing optimal converter structures to improve energy efficiency and reliability of systems in electric vehicles.
Machine learning-driven prognostics for lithium-ion batteries: enhancing RUL prediction and performance in smart energy storage systems Rajanna, Bodapati Venkata; Seenu, Aaluri; Krishnaiah, Kondragunta Rama; Peddinti, Anantha Sravanthi; Prakash, Nelaturi Nanda; Seshukumari, Bandreddi Venkata; Ambati, Giriprasad; Ahammad, Shaik Hasane; Kumar, Chakrapani Srivardhan; Rao, Allamraju Shubhangi
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp257-274

Abstract

In the evolving landscape of energy systems, batteries play a critical role in enabling hybrid and stand-alone renewable energy storage solutions. Precisely estimating battery life and remaining useful operational life will go a long way in enhancing the efficiency of the system with assured reliability in smart power storage devices. This report comprehensively surveys advanced approaches in the management of batteries through state-of-the-art artificial intelligence tools-support vector machines, relevance vector machines (RVM), long short-term memory (LSTM) models, and bayesian filters-that are being used with a view to enhancing remaining useful life (RUL) estimates and making real-time system health monitoring capabilities possible. Modeling approaches surveyed include state estimation, capacity, and thermal management, while discussing their applicability to lithium-ion batteries. The review also explores publicly available battery datasets, feature engineering strategies, and hybrid diagnostic frameworks. A technoeconomic perspective is provided to assess system performance in renewable-integrated power grids. This paper aims to consolidate current knowledge, provide comparative insights into the strengths and limitations of different approaches, and highlight open research challenges to guide future developments in smart AI-enabled battery systems that support sustainable and resilient energy infrastructure.
Co-Authors Ambati, Giriprasad Babu, Bukkapatnam Rakesh Eragamreddy, Gouthami Girija, Sakimalla Prabhakar Inthiyaz, Syed Kolukula, Nitalaksheswara Rao Krishnaiah, Kondragunta Rama Kumar, Chakrapani Srivardhan Kumar, Cheeli Ashok Najumunnisa, Mohammad Peddinti, Anantha Sravanthi Prakash, Nelaturi Nanda Rajanna, Bodapati Venkata Rajesh, Vullanki Rama Krishnaiah, Kondragunta Ramaiah, Veerlapati Rani, Prathipati Ratna Sudha Rao Kolukula, Nitalaksheswara Rao, Allamraju Shubhangi Ravikanth, Sivangi Reddy, Ganta Raghotham Seenu, Aaluri Seshukumari, Bandreddi Venkata Sudhakar, Ambarapu Venkata Rajanna, Bodapati