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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Surender Reddy Salkuti
Woosong University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Performance improvement of DC microgrids via adaptive neuro-fuzzy inference system -optimized AI-tuned fractional order proportional-integral-derivative controllers Debani Prasad Mishra; Sarita Samal; Manas Ranjan Sahu; Sonna Murari; Piyuskant Das; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp797-804

Abstract

This paper presents a novel approach to enhance the dynamic performance of direct current (DC) microgrids using an artificial intelligence (AI)-tuned fractional order proportional-integral-derivative (FO-PID) controller, further optimized through an adaptive neuro-fuzzy inference system (ANFIS). Conventional PID controllers tend to fail when it comes to dealing with microgrid environment-related non-linearities and uncertainties, particularly under changing load and generation situations. To remedy this, the suggested approach combines AI-tuned tuning algorithms for selecting initial parameters, and then ANFIS optimization to fine-tune the FOPID gains adaptively for better control precision. The performance of the hybrid control approach is tested through MATLAB simulations on a generic DC microgrid model that includes distributed energy resources, power electronic converters, and dynamic loads. Comparative evaluation against standard PID and independent FOPID controllers verifies remarkable advantages in terms of voltage regulation, stability, and transient response in various operating conditions. Amongst the achieved outcomes, it highlights the strength of the proposed ANFIS-optimized AI-tuned FOPID controller as a smart and robust strategy for real-time control of DC microgrids.
Predicting battery life performance using artificial intelligence techniques in electric vehicles Debani Prasad Mishra; Munavath Pavan Kalyan; Shivam Tyagi; Piyushjeet Piyushjeet; Shiv Grover; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp805-812

Abstract

Electric vehicles’ (EVs’ performance and sustainability are significantly influenced by the efficiency and lifespan of their lithium-ion batteries. This paper explores the critical factors affecting battery degradation, focusing on parameters such as charge cycles, thermal management, and voltage dynamics. Utilizing a dataset of 14 batteries, the study employs data-driven machine learning (ML) to predict the remaining useful life (RUL) of batteries. The ensemble-based regression model demonstrated superior predictive accuracy through comprehensive analysis, achieving R² values of 97.89% for training and 94.69% for testing. Feature importance analysis identified cycle index (CI) as the most critical determinant of battery health, followed by discharge time and voltage stability. Visualizations, including correlation heatmaps and residual plots, validate the robustness of the selected model. Additionally, sustainable charging strategies, such as steady current-steady voltage (also known as CC-CV), are highlighted for their role in enhancing battery longevity. This research offers actionable insights into battery management systems, providing a robust foundation for predictive maintenance and the development of sustainable electric mobility solutions.
Integrating IoT for advancing agriculture: innovations and implications for future surveys Debani Prasad Mishra; Rakesh Kumar Lenka; Aditya Kumar; Aditya Jasrotia; Surender Reddy Salkuti
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp891-899

Abstract

The internet of things (IoT) is revolutionizing agriculture, offering a paradigm shift in how we cultivate crops and manage livestock. By integrating IoT devices such as sensors, drones, and smart machinery into farming practices, agricultural operations gain unprecedented levels of data driven insights and control. This abstract emphasizes the pivotal role of IoT in agriculture and its far-reaching implications for the future. IoT empowers farmers with real-time information on essential factors like moisture of soil, nutrient levels, weather patterns, and health of crops, helping make accurate decisions while optimizing resources. Through IoT-enabled monitoring and automation, farmers can remotely manage irrigation, pest control, and livestock health, reducing manual labor and minimizing environmental impact. The implications of IoT in agriculture extend beyond individual farms, shaping the future of food production on a global scale. With a burgeoning world population and climate change threatening traditional farming methods, IoT offers solutions for enhancing productivity, sustainability, and resilience in the face of emerging challenges. From precision agriculture to smart supply chains, the revolutionary prospect of IoT in agriculture promises to ensure food security, economic viability, and environmental stewardship for generations to come.
Co-Authors Abhisek Sahoo Aditya Jasrotia Aditya Kumar Aditya Prasad Mahapatra Aishwarya Priyadarshini Amba Subhadarshini Nayak Ambika Prasad Hota Ambuj Shukla Ankit Gupta Anwesh Pattnaik Arghya Sardar Arun Kumar Sahoo Arun Kumar Sahoo Ashutosh Singh Chauhan Asutosh Samal Atman Panigrahi Bhabani Shankar Panda Bishweashwar Sukla Dashmat Hembram Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prasad Mishra Debani Prashad Mishra Drishana Jhunjhunwalla Harikrishnan K. M. Jayanta Kumar Sahu Jayanta Kumar Sahu Kalpa Ranjan Behera Kaushiki Agrawal Kshirod Kumar Rout Kshirod Kumar Rout Kshirod Kumar Rout Kunal Badapanda Manas Ranjan Sahu Mandakurit Nivas Mandakuriti Nivas Monalisa Panda Munavath Pavan Kalyan Neelakanteshwar Rao Battu Neelakanteshwar Rao Battu Nimay Chandra Giri Nitish Saswat Mallik P. Sravanthi Padarabinda Palai Pallavi V. Honagond Pankaj Sharma Papia Ray Piyushjeet Piyushjeet Piyuskant Das Pooja S. Pujari Prakash Kumar Ray Pranay Kumar Panda Pratyush Gupta Priyansh Kasyap Rakesh Kumar Lenka Rakesh Kumar Yadav Ramakanta Mohanty Rambilli Krishna Prasad Rao Naidu Rambilli Krishna Prasad Rao Naidu Rishabh Vishnoi Rudra Narayan Senapati Rudranarayan Senapati S. Narasimha S. Narasimha S. S. Saswat Sandeep Vuddanti Sanhita Mishra Sanhita Mishra Sarita Samal Saroj Kumar Panda Seong-Cheol Kim Shiv Grover Shivam Tyagi Sivkumar Mishra Sivkumar Mishra Sivkumar Mishra Smrutisikha Jena Somnath Banerjee Sonna Murari Sopa Mousumi Patro Soumya Ranjan Das Sravanthi Pagidipala Subhrajit Jena Suchitra Shastri Suman Patra Suman Patra Swarnodeep Kar Truptasha Tripathy V. Sandeep Varun N. John Vinod Karknalli