Advancing Electric Vehicle Battery Management: A Data-Driven Digital Twin Approach for Real-Time Monitoring and Performance Enhancement

Abstract

The global shift towards electric vehicles (EVs) represents a critical strategy for fighting climate change and reducing both reliance on fossil fuels and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$CO_{2}$</tex-math></inline-formula> emissions. In this scenario, battery management systems (BMS) become crucial in EVs to ensure battery safety, reliability, and efficiency. Recently, data-driven estimation techniques have been proposed to estimate battery-related metrics, such as remaining capacity or aging condition. These techniques emerged as an answer to batteries' intrinsic variability, but they rely primarily on full charge/discharge cycles, overlooking the nuances of partial charging, which is prevalent in real-world usage. This paper presents a novel BMS architecture for EVs, leveraging digital twin technology and data-driven modeling to address these challenges. The proposed dynamic dual-model approach seamlessly integrates real-time monitoring with cloud-based analytics to continuously evaluate and predict battery capacity degradation. Key innovations include sophisticated feature engineering and segmentation strategies, which enable precise state of health (SoH) estimation across a wide range of driving conditions. Additionally, the architecture incorporates a dynamically retraining State of Charge/Energy (SoC/SoE) model that adapts to battery aging, thereby maintaining high accuracy throughout the battery's life cycle. Extensive validation using datasets from public institutions demonstrates the effectiveness and robustness of the proposed system.