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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Applied Power Engineering (IJAPE) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Rajanna, Bodapati Venkata
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Solar and battery input super boost DC–DC converter for solar powered electric vehicle Yadagiri, Aerpula; Talagadadeevi, Srinivasa Rao; Rao, Seetamraju Venkata Bala Subrahmanyeswara; Rao, Bitra Janardhana; Inthiyaz, Syed; Prakash, Nelaturi Nanda; Rajanna, Bodapati Venkata; Kumar, Cheeli Ashok
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp479-487

Abstract

The electric vehicle (EV) is increasingly emerging as an attractive solution to reduce reliance on fossil fuels in India. In commercial EVs, solar photovoltaic (PV) technology is employed both to charge the battery and power the vehicle. However, the conventional bidirectional DC-DC converter layout results in underutilization of solar PV power when the battery's state of charge (SOC) reaches maximum capacity. This work offers a unique dual input super boost (DISB) DC-DC converter designed specifically for solar-powered electric vehicles (EVs) to address the aforementioned challenge. The recently suggested converter operates in six different modes to effectively capture solar photovoltaic (PV) power. Notable benefits of this design include a wide range of speed control and fewer conduction devices in each mode, which eventually result in increased overall efficiency. An extensive analysis of the suggested DISB DC-DC converter is carried out by the study, encompassing detailed examination of operating waveforms and dynamic evaluations. Furthermore, the converter's performance and operation under the six different modes are verified through simulation.
Power quality enhancement for a grid connected wind turbine energy system with PMSG Rajasri, Kasula; Kiranbabu, Movva Naga Venkata; Raja, Banda Srinivas; Parvez, Muzammil; Reddy, Govulla Ravi Kumar; Prakash, Nelaturi Nanda; Ahammad, Sk. Hasane; Rajanna, Bodapati Venkata
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i2.pp392-400

Abstract

This project investigates the burgeoning potential of gearless wind turbine systems as a pivotal clean energy resource. Unlike conventional gearbox-based turbines, which grapple with issues like frequent breakdowns, intricate repairs, and prolonged downtimes, gearless systems present a suite of advantages. Chief among these is heightened reliability, diminished maintenance costs, and augmented efficiency. By circumventing the need for a gearbox, gearless turbines shed weight, bolster reliability, and demand less upkeep. The incorporation of permanent magnet generators further elevates their efficiency and renders them well-suited for offshore deployment. The emergence of gearless wind turbines heralds a promising frontier for effectively and efficiently harnessing wind power. Their streamlined design and robust performance potential position them as a transformative force in the renewable energy landscape, poised to catalyze substantial advancements towards sustainable energy goals. As research delves deeper into their capabilities and optimization, gearless turbines are poised to emerge as a cornerstone technology in the global pursuit of clean energy solutions.
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.
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.
Design and memory optimization of hybrid gate diffusion input numerical controlled oscillator Reddy, Gujjula Ramana; Perumal, Chitra; Kodali, Prakash; Rajanna, Bodapati Venkata
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i1.pp78-86

Abstract

The numerically controlled oscillator (NCO) is one of the digital oscillator signal generators. It can generate the clocked, synchronous, discrete waveform, and generally sinusoidal. Often NCOs care utilized in the combinations of digital to analog converter (DAC) at the outputs for creating direct digital synthesizer (DDS). The network on chips (NOCs) are utilized in various communication systems that are fully digital or mixed signals such as synthesis of arbitrary wave, precise control for sonar systems or phased array radar, digital down/up converters, all the digital phase locked loops (PLLs) for cellular and personal communication system (PCS) base stations and drivers for acoustic or optical transmissions and multilevel phase shift keying/frequency shift keying (PSK/FSK) modulators or demodulators (modem). The basic architecture of NCO will be enhanced and improved with less hardware for facilitating complete system level support to various sorts of modulation with minimum FPGA resources. In this paper design and memory optimization of hybrid gate diffusion input (GDI) numerically controlled oscillator based on field programmable gate array (FPGA) is implemented. compared with NCO based 8-bit microchip, memory optimization of hybrid GDI numerically controlled oscillator based on FPGA gives effective outcome in terms of delay, metal-oxide-semiconductor field-effect transistors (MOSFET’s) and nodes.
Artificial intelligence-based multi-key security for protected and transparent medical cloud storage Bagadi, Ravi Kiran; Koraganji, Neelima Santoshi; Venkata Seshukumari, Bandreddi; Karuturi, Kavya Ramya Sree; Abotula, Sireesha; Rajanna, Bodapati Venkata; Annavarapu, Mahalakshmi; Kolukula, Nitalaksheswara Rao; Pinajala, Jayasree; Meka, James Stephen
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1241-1250

Abstract

Ensuring the security and privacy for the patient medical records and medical reports data is a crucial challenge as cloud-based healthcare technologies become more prevalent. For cloud-hosted medical data, internet of things (IoT) and artificial intelligence (AI) technologies shows best solutions for the challenges in the medical domain. This study suggests a Secure and Transparent Multi-Key Authentication Framework that makes use of AI. Using Z-score normalization, the framework first preprocesses the data before clustering to create a multi-level multi-key security structure. The physics-informed triangulation aggregation neural network (PITANN) model in the study reduces computation costs by minimizing overhead, ensuring secure handling of location-based and medical data for enhanced data classification and encryption effectiveness. A multi-key derivation of an elliptic curve, the ElGamal cryptography scheme is presented, which allows for safe multi-key encryption with little increase in the length of the ciphertext. This method guarantees safe, confidential access to cloud-hosted encrypted health information. An envisioned amalgamation improves flexibility by enhancing performance metrics such as speed of computation while safeguarding patient information through enhanced security measures and ensuring precise medical record integrity within virtual healthcare systems.
Artificial intelligence framework for multi-stage lung disease detection with audio signals Venkata Seshukumari, Bandreddi; Tayi, Jyothirmayi; Bhuthkuri, Rajeshkhanna; Madireddy, Bhavani; Yellapu, Jhansi; Rajanna, Bodapati Venkata; Kolukula, Nitalaksheswara Rao; Kodali, Siva Sairam Prasad; Pinajala, Jayasree; Meka, James Stephen; Rami Reddy, Chilakala
IAES International Journal of Artificial Intelligence (IJ-AI) 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/ijai.v15.i1.pp106-115

Abstract

Automated diagnostic systems are increasingly pivotal in advancing the accuracy and efficiency of medical diagnostics. Due to abnormal changes in human life and pollution, lung disease and cancer cases increasing in huge number. Identification and prediction of lung diseases may help to increase the human life span. This study introduces a robust framework for automatic lung disease detection using respiratory sound signals. The methodology brings together a series of activities like preprocessing, feature extraction, selection, and classification to improve diagnostic accuracy. The adaptive empirical stockwell-transform (AEST) is used to enhance the quality of the signal, whereby extracting and refining features, mainly Mel-frequency cepstral coefficients (MFCC), and Mel-spectrograms, are used. The scalable convolutional geyser network (SCGN) helps to mitigate challenges posed by imbalanced datasets, redundant features, and overfitting, ensuring reliable classification of the features. The model is validated when using the International Conference on Biomedical and Health Informatics (ICBHI) dataset, which validates the performance indicators of the model (F1-score 0.94, accuracy 0.95, precision 0.93, recall 0.94). This is shown superior performance compared to other existing models and demonstrates the framework's ability to diagnose a serviceable and reliable medical diagnosis; which indicates the strengths of combining advances in signal processing and scalable deep learning (DL) in healthcare applications.
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 Abotula, Sireesha Ahammad, Shaik Hasane Ahammad, Sk. Hasane Ambati, Giriprasad Annavarapu, Mahalakshmi Babu, Bukkapatnam Rakesh Bagadi, Ravi Kiran Balaswamy, Chinthaguntla Batakala, Jeevanrao Bhavana, Mukku Bhuthkuri, Rajeshkhanna Chaturvedi, Abhay Cheerla, Sreevardhan Daniel, Ravuri Eamani, Ramakrishna Reddy Eragamreddy, Gouthami Girija, Sakimalla Prabhakar Himabindu, D. Inthiyaz, Syed Kallakunta, Ravi Kumar Kallakuta, Ravi Kumar Kameswari, Yeluripati Lalitha Karthik, Nachagari Karuturi, Kavya Ramya Sree Kiranbabu, Movva Naga Venkata Kodali, Prakash Kodali, Siva Sairam Prasad Kolukula, Nitalaksheswara Rao Koraganji, Neelima Santoshi Krishnaiah, Kondragunta Rama Kumar, Chakrapani Srivardhan Kumar, Cheeli Ashok Kumar, Mugachintala Dilip Kumar, Munuswamy Siva Kumar, Yarrem Narasimhulu Vijaya Kumari, Popuri Rajani Madireddy, Bhavani Meka, James Stephen Mohan, Kaja Krishna Mohana, Thota Naidu, Madhireddi Bhaskara Najumunnisa, Mohammad Nandaprakash, Nelaturi Parvez, Muzammil Pasam, Prudhvi Kiran Peddinti, Anantha Sravanthi Perumal, Chitra Pinajala, Jayasree Prakash, Nelaturi Nanda Prasad, Bode Raja, Banda Srinivas Rajasri, Kasula Rajesh, Vullanki Rami Reddy, Chilakala Ramu, Tirunagari Bhargava Rani, Prathipati Ratna Sudha Rao, Allamraju Shubhangi Rao, Bitra Janardhana Rao, Seetamraju Venkata Bala Subrahmanyeswara Ravikanth, Sivangi Reddy, Ganta Raghotham Reddy, Govulla Ravi Kumar Reddy, Gujjula Ramana Reddy, Mula Sreenivasa Reddy, Tadi Diwakara Subba Sai, Cheepurupalli Krishna Chaitanya Seenu, Aaluri Seshukumari, Bandreddi Venkata Shashank, Ramagiri Sudarsa, Dorababu Sudhakar, Ambarapu Surendher, Guntukala Talagadadeevi, Srinivasa Rao Tayi, Jyothirmayi Venkata Seshukumari, Bandreddi Vinodhkumar, Nallathambi Yadagiri, Aerpula Yellapu, Jhansi