Trustworthy Battery Management: A Digital Twin Approach Leveraging XAI and Blockchain

Abstract

This study presents a digital twin framework for predicting the state of health (SoH) in battery management systems (BMS). This framework integrates a single particle model with electrolytes (SPME) and a long-short-term memory (LSTM) network to model battery behaviour based on a NASA battery dataset. To ensure the security of battery data, data is recorded on the Ethereum blockchain and queried when needed for secure prediction. To ensure the interpretability of the predictions, an explainable AI (XAI) approach, SHAP, is employed. Experimentation shows the viability of the proposed framework in accurately predicting the SoH of physical batteries.