Author

R. Kalaivani

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Open access Sep 2026

Accurate RUL prediction of EV batteries using random forest and ensemble learning frameworks

Precise remaining useful life (RUL) estimation for lithium-ion batteries is essential for improving the safety, reliability, and maintenance of electric vehicles (EVs). This study proposes a random forest (RF)-based ensemble learning framework using the publicly available Hawaii Natural Energy Institute (HNEI) dataset...

Ponkumar Ganesapandiyan, P. Hemachandu, N. Rajavinu et al. · International Journal of Pow... · 0 citations

Open access Sep 2026

Evaluation of hybrid and standalone learning models for predicting lithium-ion battery capacity degradation

The prediction of lithium-ion battery capacity degradation plays a vital role in ensuring safe and efficient operation in electric mobility and renewable energy applications. This paper evaluates standalone machine learning, deep learning, and hybrid models for battery capacity estimation. The evaluated ML models inclu...

Shobana Devendiren, A. Muthuraman, M. Vanitha et al. · International Journal of Pow... · 0 citations

Open access Sep 2026

A privacy-preserving IoT-machine learning framework for optimized and secure demand-side management in smart grids

This paper presents an integrated internet of things (IoT) and machine learning-based framework for secure and efficient demand-side management (DSM) in modern smart grids. The proposed approach combines long short-term memory (LSTM) networks for accurate load forecasting, federated learning (FL) for decentralized priv...

S. Pushpa, Jonnadula Narasimharao, K. L. Kishore et al. · International Journal of Pow... · 0 citations