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.

Digital twins are becoming a business imperative, covering the entire lifecycle of an asset, and forming the foundation for connected products and services. Companies that fail to respond will be left behind. Implementing a dynamic cloud model of a physical thing or system such as wind turbine that relies on live streaming data will help to understand its states, respond to changes, improve operations, and add value to its Key Performance Indicators (KPIs) such as reliability, availability, maintenance cost and associated risks. This paper contributes to build a power prediction digital twin for a wind turbine’s generic model guided by IEC 61400-25, IEC 61400-27-1-2020 (Type 4A) via utilizing the data analytics of Microsof to Azure IoT mechanisms along with decentralized decisions of Machine Learning (ML) in such way utilizing its strengths in physics-based, data-driven modeling and the hybrid analysis approaches. The proposed modeling technique can help the scientific community in building long-term maintenance models for wind farms considering maintenance opportunities and condition prediction, as well as evaluating the machine performance including maintenance costs and production losses.