Interpretable Data Driven Digital Twin Models for Estimating Electric Vehicle Battery Condition

Sivasankar Chittoor1, S Leela Krishna2, B Saranya3, D Sarath Kumar4, G Tharun5
1,2,3,4,5 Computer Science and Information Technology, Siddharth Institute of Engineering & Technology

Abstract - As the automotive industry rapidly advances towards electric vehicles (EVs), accurately predicting battery states is crucial for optimizing performance, safety, and longevity. This project presents a novel approach using Explainable Data-Driven Digital Twins topredict battery states in electric vehicles. The methodology integrates various advanced machine learning algorithms, including Deep Neural Networks (DNN), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), Support Vector Regression (SVR),Support Vector Machines (SVM), Feedforward Neural Networks (FNN), Radial Basis Function networks (RBF), Random Forests (RF),andExtreme Gradient Boosting (XG Boost).The primaryobjective of this study is to enhance the predictability of battery states by leveraging these diverse algorithms to build a comprehensive digital twin model. The model aims to provide accurate predictions of key batteryparameters such as state of charge (SOC) and state of health (SOH) under various operational conditions. Byutilizing explainable AI techniques, the project also focuses on interpreting and understanding the underlying factors influencing battery performance.