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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI)
Nitalaksheswara Rao Kolukula
GITAM University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analysis of different converter topologies for EV applications Bodapati Venkata Rajanna; Kondragunta Rama Krishnaiah; Sakimalla Prabhakar Girija; Shaik Hasane Ahammad; Mohammad Najumunnisa; Syed Inthiyaz; Gouthami Eragamreddy; Giriprasad Ambati; Nitalaksheswara Rao Kolukula
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.
Artificial intelligence framework for multi-stage lung disease detection with audio signals Bandreddi Venkata Seshukumari; Jyothirmayi Tayi; Rajeshkhanna Bhuthkuri; Bhavani Madireddy; Jhansi Yellapu; Bodapati Venkata Rajanna; Nitalaksheswara Rao Kolukula; Siva Sairam Prasad Kodali; Jayasree Pinajala; James Stephen Meka; Chilakala Rami Reddy
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.
Co-Authors Bandreddi Venkata Seshukumari Bhavani Madireddy Bodapati Venkata Rajanna Chilakala Rami Reddy Giriprasad Ambati Gouthami Eragamreddy James Stephen Meka Jayasree Pinajala Jhansi Yellapu Jyothirmayi Tayi Kondragunta Rama Krishnaiah Mohammad Najumunnisa Rajeshkhanna Bhuthkuri Sakimalla Prabhakar Girija Shaik Hasane Ahammad Siva Sairam Prasad Kodali Syed Inthiyaz