International Journal on Science and Technology

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Explainable Data Driven Digital Twins for Predicting Battery States in Electric Vehicles

Author(s) Rakesh kumar.R, Raghu raman.S, Siddharthdharan.Sa, Dr.S.Mohandoss, Dr. F. Antony Xavier Bronson
Country India
Abstract This research introduces an innovative approach to predicting battery states for electric vehicles (EVs) using an Explainable Data-Driven Digital Twin framework. As EV adoption grows, optimizing battery performance becomes critical for ensuring vehicle reliability and efficiency. The framework focuses on predicting two essential metrics—SOC and SOH— models for machine learning like SVM, SVR, RF, etc.
Keywords Electric Vehicles, Battery Prediction, Digital Twins, Machine Learning, DNN, LSTM, CNN, Support Vector Regression, Random Forests, XGBoost.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-28
Cite This Explainable Data Driven Digital Twins for Predicting Battery States in Electric Vehicles - Rakesh kumar.R, Raghu raman.S, Siddharthdharan.Sa, Dr.S.Mohandoss, Dr. F. Antony Xavier Bronson - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.3023
DOI https://doi.org/10.71097/IJSAT.v16.i1.3023
Short DOI https://doi.org/g9dgp2
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