A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling

TL;DR

This paper presents a data-driven digital twin model for EV batteries that accurately tracks battery health and charge status in real-time, addressing aging and performance issues to improve EV reliability.

Contribution

It introduces a novel battery digital twin framework utilizing machine learning models for real-time health and charge estimation, with demonstrated effectiveness on public data.

Findings

High accuracy in battery state estimation

Efficient inference and retraining times for onboard use

Effective modeling of battery aging and dynamics

Abstract

The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over time. For this reason, monitoring the battery State Of Charge (SOC) and State Of Health (SOH) during the EV lifetime is a very relevant problem. This work proposes a battery digital twin structure designed to accurately reflect battery dynamics at the run time. To ensure a high degree of correctness concerning non-linear phenomena, the digital twin relies on data-driven models trained on traces of battery evolution over time: a SOH model, repeatedly executed to estimate the degradation of maximum battery capacity, and a SOC model, retrained periodically to reflect the impact of aging. The proposed digital twin structure will be exemplified on a public dataset to motivate its adoption and prove its effectiveness, with high accuracy and inference and retraining times compatible with onboard execution.