A Data-Driven Digital Twin of Electric Vehicle Li-Ion Battery State-of-Charge Estimation Enabled by Driving Behavior Application Programming Interfaces

• Published in: Batteries (Basel) Vol. 9; no. 10; p. 521
• Main Authors: Issa, Reda, Badr, Mohamed M., Shalash, Omar, Othman, Ali A., Hamdan, Eman, Hamad, Mostafa S., Abdel-Khalik, Ayman S., Ahmed, Shehab, Imam, Sherif M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Algorithms; Application programming interface; Artificial intelligence; Automobiles, Electric; Cloud computing; Data analysis; Data collection; Data entry; Decision making; digital twin; Digital twins; Digitization; electric vehicle; Electric vehicles; Energy management; Estimation; Global positioning systems; GPS; Industrial applications; Internet of Things; Lithium cells; Lithium-ion batteries; lithium-ion battery; Machine learning; Methods; Operating temperature; Predictive maintenance; Rechargeable batteries; Reliability (Engineering); State of charge; Time synchronization; Egypt
• ISSN: 2313-0105; 2313-0105
• DOI: 10.3390/batteries9100521

Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs’ SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study’s results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications.