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

Author:

Issa Reda1ORCID,Badr Mohamed M.12ORCID,Shalash Omar13ORCID,Othman Ali A.1ORCID,Hamdan Eman4ORCID,Hamad Mostafa S.1ORCID,Abdel-Khalik Ayman S.5ORCID,Ahmed Shehab6ORCID,Imam Sherif M.7ORCID

Affiliation:

1. Research and Innovation Center, Arab Academy for Science, Technology and Maritime Transport, Alamein 51718, Egypt

2. Department of Mechatronics Engineering, Alexandria Higher Institute of Engineering and Technology, Alexandria 21311, Egypt

3. College of Artificial Intelligence, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt

4. Department of Marine Engineering Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt

5. Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt

6. CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia

7. Department of Electrical Power Engineering, Faculty of Engineering, Damanhour University, Damanhour 22511, Egypt

Abstract

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.

Funder

Information Technology Industry Development Agency (ITIDA)-Egypt

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Electrochemistry,Energy Engineering and Power Technology

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