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Subbarao Mopidevi on Muck Rack

Subbarao Mopidevi

As seen in: Nature, MDPI
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Articles

A hybrid BiLSTM-MPC predictive control strategy for energy and thermal management of electric vehicles with real-time validation

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By Subbarao Mopidevi, Sreedhar Reddy Aemalla
| Nature Verified
Abstract Energy management efficiency in battery electric vehicles (xEVs) is vital for enhancing their driving performance, battery safety, and energy management efficiency. Traditional controllers, such as PID, Fuzzy logic, ANN, and traditional Model Predictive Control (MPC), have provided an acceptable performance level.
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Digital-twin-enabled reinforcement learning for energy-efficient and reliability-aware software-defined electric vehicles

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By Rahul Wilson Kotla, Sreedhar Jadapalli, S. Madhavi, Subbarao Mopidevi, T. S. N. G. Sarada Devi, Polamraju V. S. Sobhan
| Nature Verified
Abstract Software-defined electric vehicles (SDEVs) require intelligent control frameworks that simultaneously optimize energy efficiency and powertrain reliability. However, existing studies typically treat reinforcement learning, digital twins, and predictive health monitoring as separate problems, limiting real-time reliability-aware decision-making.
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RETRACTED: Narasipuram et al. Analysis of Scalable Resonant DC-DC Converter Using GaN Switches for xEV Charging Stations. World Electr. Veh. J. 2024, 15, 218

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By Rajanand Patnaik Narasipuram, Subbarao Mopidevi, Anton Dianov, Amit Tandon
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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