Aug 2026· International Conference on Information Security and Cryptology· pp. 40-46· 0 citations· 19 references
Abstract
A considerable problem posed on the operational front for Battery Energy Storage Systems (BESS) is the intermittency and non-linearity of renewable sources such as solar and wind. The traditional battery management systems are based on deterministic rule-based methods, which contribute to the inaccuracy of estimation of state of charge (SOC) and state of health (SOH) under dynamic conditions. The proposed predictive framework is based on machine learning to deal with integrating renewable energy into battery systems, using multivariate time series to help in modelling, feature normalization, artificial neural networks for nonlinear mapping, coupled with adaptive control to make real-time estimation. The SOC and SOH accuracy as per MATLAB-based verification with dynamic load and generation profiles turned out to be 97% and 96%, respectively, with 35% of error reduction. The proposed system promotes energy conservation, opens an easy channel for maintenance and enhances the life of a battery, making it a results-driven and scalable solution for smart grids and electric vehicles.
This paper presents an integrated hardware–software framework for real-time SOC estimation and intelligent power control using a machine learning approach and demonstrates high prediction accuracy with a mean absolute error of 0.0062% and R2 of 99.9997%, outperforming conventional and deep learning-based approaches.
P. David, S. Vani, S. Kavitha et al.· Engineering Research Express· 0 citations
Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and longevity of electric vehicles, battery energy storage systems, and other energy applications. This paper presents a comprehensive review of capacity-based SOH estimation algorithms, focusing...
Manh-Kien Tran, Kintak Raymond Yu, D. MacNeil· Batteries· 0 citations
For the purpose of assuring the safety, dependability, and prolonged lifespan of energy storage systems in electric vehicles, it is vital to have an accurate estimation of the level of charge of the lithium-ion battery. Attempts to capture the complicated and non-linear behaviors of batteries are frequently unsuccessfu...
S.Kanthalakshmi G.R.Radhika· Lex Localis-journal of Local...· 0 citations
In this document, an advanced intelligent energy storage management system solution for clean energy sources from solar power will be outlined through the use predictive controls. The integrated intelligent energy management architecture includes a solar panel array, battery bank, local users, two-way converter, and ut...
Shokhrukh Shoyimov, Zufar Qoryog’diyev· EPJ Web of Conferences· 0 citations
The Machine Learning-Based Intelligent Energy Management System (ML-IEMS) proposed in this paper combines a hybrid CNN-LSTM model for short-term load and renewable generation forecasting with a Reinforcement Learning dispatch agent for real-time storage, demand response, and grid exchange scheduling.
N. Suganthi, S. Tamilselvan· ITM Web of Conferences· 0 citations
Improving the performance and lifespan of lithium-ion batteries is a key challenge for the development of electric vehicles. However, accurately estimating the state of charge (SoC), state of health (SoH), and life cycle remains complex due to the electrical, thermal, and aging phenomena associated with these energy st...
Abdel-Hamid Mahamat Ali, Luc Vivien Assiene Mouodo, Paune Félix et al.· Applied Sciences· 0 citations
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