Sustainable Energy Storage System: A Metrological and AI-Based Control Approach

Authors

  • Aaluri Seenu Professor, Department of CSE, Shri Vishnu Engineering College for Women, India.
  • Shanker Shalini Assistant Professor, Department of Computer Science and Engineering, St.Joseph's Institute Of Technology, India
  • Selciya Selvan Assistant Professor, Department of ECE, Chennai Institute of Technology, India
  • Sasikala G Professor, Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India
  • Dharmesh Sur Associate professor, Department of Chemical Engineering, Marwadi University, India.
  • Priyanka Vikas Javkar Lecturer, Department of computer science, Sou.Venutai Chavan Polytechnic College, India
  • R. Senthamil Selvan Associate Professor, Department of ECE, Annamacharya Institute of Technology and Sciences, India

DOI:

https://doi.org/10.63278/10.63278/mme.v31.1

Keywords:

Artificial Intelligence, Energy Storage Systems, Machine Learning, Green Energy, Energy Sources.

Abstract

Energy storage systems (ESS) play an essential role for improving the longevity, dependability, and efficiency of power systems. Manufacturers accomplish this by providing grid support services and reducing the unpredictability of green energy sources. Because energy markets and grid conditions constantly shift and the many components of the system interact in complex ways, it is still challenging to get ESS to function and be regulated as effectively as possible. Artificial Intelligence (AI) is thus emerging as a promising means of enhancing ESS control techniques, offering smart and adaptable solutions to these challenging problems. This study examines many AI-based control strategies for improving the performance of energy storage devices. The most recent developments in deep learning, machine learning, reinforcement learning (RL) and evolutionary algorithms for ESS control are examined. It demonstrates their capacity to real-time adjust control techniques, understand intricate patterns from historical data, and capture nonlinear system dynamics. By mixing AI methods with normal optimisation and control algorithms, the study additionally addresses about how to make ESS work faster and more reliably. To lower high loads, balance loads, control frequency, and add green energy, this article addresses a few ways AI-based ESS control can be employed. The accuracy, effectiveness, and stability of energy sources might be enhanced by AI's potential to change the way energy storage systems are designed and operated.

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How to Cite

Aaluri Seenu, Shanker Shalini, Selciya Selvan, Sasikala G, Dharmesh Sur, Priyanka Vikas Javkar, and R. Senthamil Selvan. 2025. “Sustainable Energy Storage System: A Metrological and AI-Based Control Approach”. Metallurgical and Materials Engineering 31 (1):521-31. https://doi.org/10.63278/10.63278/mme.v31.1.

Issue

Vol. 31 No. 1 (2025)

Section

Research

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Copyright (c) 2025 Aaluri Seenu, Shanker Shalini, Selciya Selvan, Sasikala G, Dharmesh Sur, Priyanka Vikas Javkar, R. Senthamil Selvan

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