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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE)
Giriprasad Ambati
VNR Vignana Jyothi Institute of Engineering and Technology
Control & Systems Engineering Electrical & Electronics 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.
Machine learning-driven prognostics for lithium-ion batteries: enhancing RUL prediction and performance in smart energy storage systems Bodapati Venkata Rajanna; Aaluri Seenu; Kondragunta Rama Krishnaiah; Anantha Sravanthi Peddinti; Nelaturi Nanda Prakash; Bandreddi Venkata Seshukumari; Giriprasad Ambati; Shaik Hasane Ahammad; Chakrapani Srivardhan Kumar; Allamraju Shubhangi Rao
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 Aaluri Seenu Allamraju Shubhangi Rao Anantha Sravanthi Peddinti Bandreddi Venkata Seshukumari Bodapati Venkata Rajanna Chakrapani Srivardhan Kumar Gouthami Eragamreddy Kondragunta Rama Krishnaiah Mohammad Najumunnisa Nelaturi Nanda Prakash Nitalaksheswara Rao Kolukula Sakimalla Prabhakar Girija Shaik Hasane Ahammad Syed Inthiyaz