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

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 to predict 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), and Extreme Gradient Boosting (XGBoost).

The primary objective 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 battery parameters such as state of charge (SOC) and state of health (SOH) under various operational conditions. By utilizing explainable AI techniques, the project also focuses on interpreting and understanding the underlying factors influencing battery performance.

Our approach combines the strengths of different algorithms to improve prediction accuracy and robustness. Preliminary results indicate that the integrated model significantly outperforms traditional methods in terms of prediction accuracy and reliability. This research contributes to the development of more intelligent and adaptive battery management systems, which are essential for the future of electric mobility.

Keywords: Electric Vehicles, Battery State Prediction, Digital Twins, Machine Learning, Deep Neural Networks, LSTM, CNN, Support Vector Regression, Random Forests, Extreme Gradient Boosting.