Work

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

Year: 2025

Type: article

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.

Source: International Journal on Science and Technology

DOI: 10.71097/ijsat.v16.i1.3023

Authors: Rakesh kumar.R -, Raghu raman.S -, Siddharthdharan.Sa -, Dr.S.Mohandoss -, Dr. F. Antony Xavier Bronson -

Institution: Dr. M.G.R. Educational and Research Institute

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: hybrid

OpenAlex ID: W4409194707