A digital twin is a virtual representation of a physical object or process capable of collecting information from the real environment to represent, validate and simulate the physical twin’s present and future behavior. It is a key enabler of data-driven decision making, complex systems monitoring, product validation and simulation and object lifecycle management. As an emergent technology, its widespread implementation is increasing in several domains such as industrial, automotive, medicine, smart cities, etc. The objective of this systematic literature review is to present a comprehensive view on the DT technology and its implementation challenges and limits in the most relevant domains and applications in engineering and beyond.

https://www.mdpi.com/2072-4292/14/6/1335/pdf?version=1646876144

Digital Twin Technology Challenges and Applications: A Comprehensive Review

A newly introduced term in the field of simulating an artificial or physical system is that of the “Digital Twin” concept method. It employs a digital representation and modeling method, capable of expanding and improving the life cycle of complex items, systems, and processes. Nowadays, digital twin technology has become a key research field worldwide. In this context, it is applied and utilized in various fields. One such field is the automotive industry, a technological field that has great implications in users’ everyday life. Digital twin technology not only has great contributions from the initial stages of design until the final construction stages of vehicles, but also during its use, drawing useful information from its daily functions and making the driving experience more enjoyable, comfortable, and safe. It is worth noting that the vehicles that can greatly benefit from the use of digital twins are electric vehicles, which has tended to acquire greater shares in the last decade.

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Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence

Mass marketing of battery-electric vehicles (EVs) will require that car buyers have high confidence in the performance, reliability and safety of the battery in their vehicles. Over the past decade, steady progress has been made towards the development of advanced battery diagnostic and prognostic technologies using data-driven methods that can be used to inform EV owners of the condition of their battery over its lifetime. The research has shown promise for accurately predicting battery state of health (SOH), state of safety (SOS), cycle life, the remaining useful life (RUL), and indicators of cells with high risk of failure (i.e., weak cells). These methods yield information about the battery that would be of great interest to EV owners, but at present it is not shared with them. This paper is concerned with the present status of the information available on the battery with a focus on data-driven diagnostic and prognostic approaches, and how the information would be generated in the future for the millions of EVs that will be on the road in the next decade. Finally, future trends and key challenges for the prognostics and health management of the batteries in real-world EV applications are presented from four perspectives (cloud-edge interaction, full-scale diagnosis, artificial intelligence and electronic health reports) are discussed.

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Electric Vehicle Batteries: Status and Perspectives of Data-Driven Diagnosis and Prognosis

Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions.

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The Hitchhiker's Guide to Fused Twins: A Review of Access to Digital Twins In Situ in Smart Cities

In the era of digitalization, many technologies are evolving, namely, the Internet of Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety of sectors, including the mining industry. The use of DT in the mining industry is driven by its potential to improve efficiency, productivity, and sustainability by monitoring performance, simulating results, and predicting errors and yield. Additionally, the increasing demand for individualized products highlights the need for effective management of the entire product lifecycle, from design to development, modeling, simulating, prototyping, maintenance and troubleshooting, commissioning, targeting the market, use, and end-of-life. However, the problem to be overcome is how to successfully integrate DT into the mining business. This paper intends to shed light on the state of art of DT case studies focusing on concept, design, and development. The DT reference architecture model in Industry 4.0 and value-lifecycle-management-enabled DT are also discussed, and a proposition of a DT multi-layered architecture framework for the mining industry is explained to inspire future case studies.

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Generic Multi-Layered Digital-Twin-Framework-Enabled Asset Lifecycle Management for the Sustainable Mining Industry

A Digital Twin is a virtual representation of a real dynamic system that can simulate its current conditions, predict its future behavior, and log valuable information about its internal operation and interactions with other systems. A key feature is the capability of automatic bidirectional information flow between virtual and physical worlds. The objective of this work is to create a Living Lab for the demonstration of interacting urban Digital Twins under the United Nations’ Sustainable Development Goals of sustainable cities and communities, health and wellbeing, and industry, innovation, and infrastructure. By using a network of sensing devices mounted on a vehicle, the proposed system is capable of processing real-life data through edge computing, modeling software and Machine Learning algorithms. With the processed information, a 3D virtual representation of urban spaces and the vehicle itself, the interactions between both subjects and the evolution of each is enabled. This approach of Digital Twin technology for urban spaces has significant value when it comes to analyzing a community’s evolution, mobility, a vehicle’s dynamic behavior, and its interaction with urban infrastructure. This work presents the proposed methodology for developing Digital Twin concepts for urban spaces and vehicles as well as their respective characteristic components.

Digital Twin for Urban Spaces: an Application

Real-time pattern and activity recognition techniques have experienced a recent surge in the use of advanced statistical techniques for analyzing data. More explicitly, Machine Learning classification methods have been used in a variety of applications and for different purposes, such as recognizing different types of events from accelerometer data (e.g. in smart watches, wearables, and vibration analysis). The objective of this work is to present a novel use of accelerometer-based pattern recognition techniques to detect the urban space infrastructure characteristics based on their effect on a vehicle’s body frame dynamics. For this study, we focus on detecting speed bumps, potholes and curves based on a real-time data streaming with the objective of rendering a street digitalization to automatically update an urban space Digital Twin. The present study has achieved real-time event detection for an urban trajectory with high accuracy which yields data that can be later fused with other sensors data. This approach is effectively contributing to the infrastructure layer of a multi-layered approach of digital modelling. Furthermore, the results contribute to expand the body of knowledge of real-time accelerometer event recognition techniques.

D. Botín-Sanabria

Papers

Digital Twin Technology Challenges and Applications: A Comprehensive Review

Digital Twin for Urban Spaces: an Application

Real-time Accelerometer-Based Event Detection Through KNN Classification Method