Analysis of Digital Twins and Application Value of Power Engineering Based on BIM

• Examined various battery types, BMS architectures, and modelling techniques. • Explored deterministic, non-deterministic, and hybrid SOC estimation methods. • Discussed SOH estimation and RUL prediction using various methods. • Exploration of digital twin technologies and battery digital twins (BDTs) for enhancing BMS functionality. • Discussion of future research directions in battery state estimation. Accurate state estimation is fundamental for the safety, reliability, and performance of electric vehicle (EV) batteries. This review highlights the critical need for precise evaluation of key battery states such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL). It surveys the existing modelling and estimation techniques used in lithium-ion battery (LIB) systems. It explores parameter identification methods and the challenges associated with real-time monitoring and predictive diagnostics in dynamic EV environments. The paper further investigates the transformative role of digital twin (DT) technologies in addressing these challenges. By integrating battery digital twins technologies(BDTs): Internet of Things (IoT), cloud computing, artificial intelligence (AI), and extended reality (XR), it offers a virtual replica of the battery system. That enables continuous monitoring, predictive maintenance, and performance optimization. The review delves into the architecture, functions, challenges and future perspectives of battery digital twins (BDTs). And it emphasizes their potential to enhance traditional battery management systems (BMS) through intelligent, adaptive control. Through comparative analysis and identification of research gaps, this review provides a roadmap for future advancements in state estimation and digital twin integration.

Background: With the rapid development of digital twinning technology, the compatibility of digital twinning technology to other technologies is continuously enhanced. It is because of this that the application of image recognition technology in digital twinning technology becomes a reality. However, the key technology of digital twin, the virtual-actual mutual control technology, is not mature enough, and the image recognition technology applied in digital twin technology also has the problem of coordinate system transformation, which becomes an important link of image recognition technology in digital twin. Methods: Based on the above two problems, we take the tangram splicing project as an example to realize a virtual-actual mutual control method, so that the digital twinning technology can be well presented. Furthermore, we implement an image recognition applied to the conversion of digital twinning technology, so that digital twinning technology and image recognition have a seamless connection, allowing the application range of digital twinning technology to be further expanded. Results: In this paper, image recognition technology is successfully applied to digital twin technology by adopting the conversion between different coordinate systems and the real and virtual real time control, which makes the applicability of digital twin technology in high-tech fields such as smart factories and smart manufacturing to a higher level. Conclusions: Finally, through the tangram splicing project, the motion trajectories of the robotic arm and the tangram are consistent with the virtual robotic arm and the tangram in the computer. It is proved that our method can well combine digital twinning technology and image recognition technology.

The growing interest in Digital Twin (DT) Technology represents a significant advancement in academic research and industrial applications. Leveraging advancements in Internet of Things (IoT), sensors, and communication devices, DTs are increasingly utilised across different sectors, notably in the energy domain such as Power Systems and Smart Grids. DT concepts facilitate the creation of virtual models mirroring physical assets, streamlining real-time data management and analysis. Driven by the potential of DTs to revolutionise energy systems, this paper offers a comprehensive review of DT applications in the power sector, specifically within next-generation energy systems like Smart Grids. TThe integration of DT technology with Machine Learning (ML) algorithms is highlighted as a key factor in significantly enhancing the performance and capabilities of these advanced energy systems. In contrast to prior reviews, our study meticulously investigates all of the crucial components of energy systems, including forecasting, anomaly detection, and security, which are fundamental for improving the management of operational grids. In addition, the study examines the seamless incorporation of Renewable Energy into current grids and investigates how DT technology could contribute to Electric Vehicles for increased sustainability and reliability within the Smart Grid framework. This review underlines that DTs significantly enhance the management of real-time data and analysis, consequently improving operational grid management. There are ample opportunities into further research and development to design a more advanced and digital system as compared to conventional power systems. The findings are presented in clear and concise tables, highlighting current limitations, proposing effective solutions, and identifying potential future research directions in academia and industry.

In the realm of proactive medical care, digital twin technology is rising to the forefront as a paradigm shift due to the combination of customised healthcare, 6G connectivity, and artificial intelligence (AI). This study investigates AI-driven, 6G-powered healthcare digital twin technology, focusing on patient-centric monitoring and predictive analytics. It must clearly outline the research objectives, such as real-time health data integration, adaptive decision-making, and improved treatment personalization. The proposed methodology should describe the use of 6G-enabled ultra-reliable low-latency communication, AI models for digital twin construction, and secure data management frameworks. Improved diagnostic accuracy, fast response time to clinical emergencies, and improved resource allocation in healthcare systems were main outcomes that are presented in this abstract. The technical contributions of this work with the integration of AI, 6G, and digital twin technologies are highlighted in the end of this study, and this may support the next-generation intelligent healthcare solutions. For self-learning healthcare digital twin, a model that leverages 6G-enabled medical informatics, real-time data streams, and artificial intelligence (AI) analytics is presented in this study. This presented model may support in clinical diagnosis, and classifying damages earlier. A real-time updated digital copy of the patient was created by the suggested method by integrating multimodal data from various sources including imaging, vital signs, consumer electronic devices such as smartwatches, wearable sensor metrics, and electronic health records (EHRs). The patient physiological patterns and previous injury profiles are constantly learnt by the integration of digital twin and deep learning (DL) algorithms. This integration is effective in predicting the occurrence, kind, and severity of injuries and internal trauma, falls, cardiovascular events, and neurodegenerative insults. The digital twin and healthcare providers are connected seamlessly with 6G communication infrastructure, guaranteeing ultra-low latency and great dependability. This allows for pre-hospital and critical care settings to implement instantaneous feedback loops, as well as for remote diagnostics and adaptive clinical decision-making. From home health monitoring solutions to smartphones and wearables, consumer electronics are ubiquitous and serve as common data collectors, greatly increase access and enabling the instant collection of accurate data using a digital twin. To keep data private while training AI models on dispersed healthcare data, the architecture offers federated learning. Based on severity level, the injury types are classified by a multi-layered decision engine, and this may promote a customized, context-aware responses like alerting emergency teams, modifying treatment regimens, or demanding robotic surgical support. The AI-driven, 6G-powered healthcare digital twin revolutionizes the diagnosis of injury, and it establish a path for a precise, preventive, and predictive medicine. Furthermore, scaling efficiencies were reduced by 95.81%, while reaction times decreased to 96.88%, resulting in significant gains. This technology is particularly helpful for populations who are elderly, at risk, or located far away, as it decreases reaction time, reduces diagnostic error, and provides scalable, continuous care.

Purpose The research provides new strategies for enhancing resilience in pharmaceutical manufacturing by addressing the dynamic nature of digital technology within the challenging framework of a VUCA (volatility, uncertainty, complexity and ambiguity) world, focusing on the concept of digital twins (DTs). This paper aims to emphasize the pivotal role of resilience in navigating disruptions and propose future research directions to contribute to the industry’s effective adaptation to uncertainties and dynamic challenges. Design/methodology/approach This research uses a qualitative approach, drawing insights from an extensive literature review on pharmaceutical manufacturing resilience and the integration of digital technologies. Methodologically, it synthesizes information from scholarly articles, empirical studies and industry reports to construct a comprehensive understanding. This design facilitates a thorough examination of the subject, providing valuable insights for practitioners, researchers and policymakers in the pharmaceutical industry. Findings This paper illustrates the practical application of DTs in vaccine production, emphasizing their critical role in ensuring quality, safety and adaptability. It adopts the “seizing, sensing and transforming” framework, showcasing the strategic integration of Industry 4.0 technologies to address dynamic challenges. Key insights include the significance of human elements alongside technological advancements, the need for real-time monitoring using Internet of Things (IoT) and artificial intelligence (AI) and the transformative potential of sustainability practices, diversified supply chains and collaborative ecosystems. Research limitations/implications This paper highlights the integration of human and digital elements, emphasizing IoT, AI and DTs for resilience. It underscores sustainable practices, supply chain diversification and collaboration as key strategies for addressing pharmaceutical industry challenges. Practical implications This paper offers practical insights for pharmaceutical manufacturing, emphasizing the need for robust cybersecurity measures, strategic investments in digital technologies and proactive regulatory compliance. Managers are encouraged to allocate budgets for digital integration, prioritize change management for technology adoption and invest in a skilled workforce. The adoption and management of DTs are highlighted to enhance resilience. Overall, the paper guides pharmaceutical organizations in navigating the complexities of the VUCA environment, ensuring the quality and safety of products through a multifaceted approach that combines advanced technologies with sustainability practices, supply chain diversification and collaborative ecosystems. Social implications The research addresses VUCA challenges, including volatility, uncertainty, complexity and ambiguity in pharmaceutical manufacturing. It emphasizes societal benefits such as uninterrupted drug supply, environmental responsibility through sustainable practices and enhanced collaboration among stakeholders. By leveraging digital technologies and resilience strategies, the study highlights the pharmaceutical sector’s pivotal role in managing health crises and ensuring patient safety. Originality/value This paper offers substantial originality and value by presenting a comprehensive framework for bolstering resilience in pharmaceutical manufacturing within the VUCA environment. The integration of advanced digital technologies, notably DTs, is explored in-depth, providing novel insights into their practical application. The “seizing, sensing and transforming” concept contributes a nuanced approach, considering both technological and human dimensions. This paper addresses the gaps in current literature by combining diverse elements such as Industry 4.0, risk assessment, sustainability practices and collaborative ecosystems. Its practical implications for managers and detailed exploration of research directions add tangible value, guiding future endeavors in enhancing pharmaceutical industry resilience.

The Halal supply chain, which encompasses the sectors of food, medicines, cosmetics, has enormous challenges to comply with Islamic rules, maintain product traceability and combating fraud. Given the increasing global demand for Halal products, the demand for technology innovations to enhance supply chain transparency and ensure Halal compliance is growing. This paper explores the application of Digital Twin (DT) technology in Halal supply chain management, highlighting its ability to address these challenges. The main research question of this paper is as follows: How can Digital Twin technology facilitate transparency, traceability, and compliance in the Halal supply chains? The current study employs a qualitative methodology through a review of the extant literature and reviews of industrial case studies. The study reveals that the Digital Twin technology promises to considerably improve the operational excellence of Halal supply chains through real-time Halal status monitoring ensuring that products comply with religious and ethical requirements along the supply chain. The main applications include tracking Halal certification, preventing cross-contamination, and enhancing transparency for certification bodies, manufacturers, and consumers. The results indicate that Digital Twin technology has the potential to transform Halal supply chains by reducing fraud, improving operational efficiencies, and building consumer trust. As a comprehensive Halal compliance solution, Digital Twin can enable a more transparent and trustworthy Halal certification process on a global scale. The study extends the knowledge body on technology innovations in supply chain management especially with religious and ethical product certifications and sets the stage for future research in the area.

This research presents an innovative approach to revolutionize IoT service development in medical education, specifically designed to empower individuals with physical disabilities. By integrating digital twin technology, we offer dynamic virtual representations of tangible assets, facilitating real-time simulation, monitoring, and feedback. A unique visual response algorithm has been developed to enhance the processing of visual vector data, resulting in a more efficient IoT service development process. Our method demonstrates superior performance over traditional techniques, particularly in achieving higher intrinsic variable merging values, which is critical for accurate and accessible visualization. The practical applications of this technology are highlighted through case studies that demonstrate how physically disabled students can benefit from interactive and immersive educational experiences. For instance, students can engage with the digital twins of medical equipment, allowing them to practice procedures and gain hands-on experience in a virtual environment without physical barriers. This approach not only improves accessibility but also personalizes learning experiences, adapting to the unique needs of each student. The research underscores the importance of inclusive design in developing IoT services, ensuring higher inclusivity rates and addressing diverse learning patterns. The findings suggest that the integration of IoT and digital twin technologies can significantly enhance medical education, making it more accessible, effective, and inclusive for physically disabled individuals. This study lays the groundwork for future advancements in this field, highlighting the potential for ongoing technological innovations to further transform medical education.

To overcome the technical bottleneck faced by the traditional equipment battle damage assessment method by analyzing the application status of digital twins in battle damage assessment, the application needs and the existing problems of current digital twin technology in damage assessment were summarized. Relying on battle damage tests, based on combing the current status of equipment battle damage test evaluation and digital twin technology research, the connotation and application features of digital twinning technology-oriented to equipment battle damage test assessment were explored. The architecture and implementation plan of the digital twin oriented to equipment battle damage test assessment were structured. The key technology and realization of digital twin oriented to battle damage test assessment were proposed. This study provided a theoretical reference and method guidance for the application of digital twins in battle damage assessment, which is of great reference significance for the development of digital twin battlefield construction and battle damage assessment.

As the Internet of Things (IoT) revolutionizes intelligent environments, energy efficiency remains a critical challenge. Traditional IoT-based energy management systems rely on predefined rule-based automation, which lacks adaptability to dynamic environmental conditions. This study proposes an Agentic AIoT system that integrates AI Agents, Large Language Models (LLMs), and Digital Twin technology to enhance IoT decision-making and optimize energy consumption in smart homes. Unlike conventional IoT frameworks, our system dynamically analyzes contextual factors—including real-time environmental conditions, user behaviors, and external data sources (e.g., weather forecasts and occupancy schedules)—to generate optimal energy-saving decisions autonomously. The proposed system consists of three key components: (1) Information Modules, which aggregate multi-source data beyond traditional IoT sensor inputs and construct a Digital Twin model for real-time environment simulation; (2) AI Agents & LLMs, which process contextual data and generate adaptive energy-saving strategies through intelligent reasoning; and (3) Automated Decision Execution, which seamlessly integrates optimized actions into IoT operations. By incorporating Digital Twin technology, the system can simulate various energy consumption scenarios, predict future power demands, and refine decision-making through AI-driven iterative learning. Experimental results demonstrate that our AIoT system achieves a 47.77% reduction in energy consumption compared to non-IoT environments while enhancing automation, adaptability, and user-centric control. Unlike conventional rule-based IoT solutions, integrating Digital Twin with LLM-powered AI Agents enables real-time context-aware decision-making and predictive energy optimization. This study contributes to intelligent IoT applications, Digital Twin-driven energy management, and AI-enhanced automation. Future research will focus on refining AI-driven autonomous decision-making, improving Digital Twin’s predictive accuracy, and further quantifying the impact of LLM-powered AIoT systems on energy efficiency. Our findings highlight the trans-formative potential of LLM-integrated AIoT with Digital Twin technology in revolutionizing energy management for intelligent environments while ensuring seamless interoperability with existing IoT infrastructures.

A digital twin (DT) system is a virtual system that can provide a comprehensive description of a real physical system. The DT system continuously receives data from physical sensors and user input information and provides information feedback to the physical system. It is an emerging technology that utilizes an advanced Internet of Things (IoT) to connect different objects, which is in high demand in various industries and its research literature is growing exponentially. Traditional physical systems provide data support for the monitoring of physical objects such as buildings through digital modeling techniques, data acquisition tools, human computer interfaces, and building information models (BIM). However, DT can offer much more than data presentation. DT uses the received data to perform operations such as analysis, prediction, and simulation, and finally transmits the analysis results to the physical system as feedback. Compared with other physical systems, DT has the characteristics of bidirectional data exchange and real-time autonomous management. The plant factory control system based on digital twin technology continuously measures the power consumption of electrical equipment through the sensors of the physical system and makes the corresponding virtual color-coded gradient map based on the obtained data. The darker the virtual device is, the more power it currently requires, and just based on the shade of color gives the user a very intuitive idea of the current power usage of the electronic device. There has been extensive research on digital twin technology, but there are few studies on implementing plant factories based on digital twin technology. This paper proposes the idea of combining digital twin technology with plant factories to provide research directions for future smart agriculture. It proves that smart agricultural production with sustainability can also benefit from this idea.

The application of digital twin technology in healthcare has emerged as a significant advancement, offering new possibilities for personalized medicine, surgical planning, and healthcare system optimization. By creating real-time, data-driven virtual models of patients, organs, and hospital environments, digital twins enable precise simulations, predictive analytics, and personalized treatment strategies. This review and meta-analysis evaluate the role of digital twins in improving clinical decision-making, patient outcomes, and operational efficiency while also addressing the technical, ethical, and regulatory challenges that hinder their widespread adoption. The analysis synthesizes data from studies examining digital twins in disease modeling, surgical rehearsals, and hospital resource management, highlighting their ability to enhance patient-specific interventions and optimize healthcare workflows. The results suggest that digital twins significantly improve diagnostic accuracy, treatment efficacy, and hospital efficiency, demonstrating their potential as transformative tools in modern medicine. However, challenges such as data integration complexities, patient privacy concerns, computational demands, and regulatory uncertainties must be addressed to ensure reliable implementation. This study underscores the importance of further research, cross-disciplinary collaboration, and the establishment of standardized regulatory frameworks to facilitate the responsible integration of digital twin technology in healthcare. With continued advancements and appropriate safeguards, digital twins can contribute to more precise, efficient, and patient-centered healthcare delivery, paving the way for a new era of medical innovation.

Intelligent manufacturing, at the forefront of contemporary production systems, provides improved quality, increased productivity, cost effectiveness, and expanded flexibility in manufacturing processes. The rise of sustainability is propelling the rapid advancement of sustainable manufacturing processes. Digital twin technology has become a crucial asset in intelligent manufacturing for a sustainable future, facilitating real-time monitoring of system states and predictive maintenance to anticipate potential failures for sustainability developments in production systems. This research highlights the real benefits of digital twin technology in Industry 4.0 and shows how it can be used to promote sustainable production. This chapter examines the components of intelligent manufacturing, including equipment, systems, and services. Subsequently, it discusses the sustainability aspects inherent in intelligent manufacturing and sustainability in production systems. The next section of this chapter introduces digital twin technology for manufacturing systems, highlighting its applications and its role in advancing intelligent manufacturing. Finally, it presents insights into the current landscape and future trajectories of intelligent manufacturing, underscoring the transformative potential of digital twin integration in line with Industry 4.0.

Digital Twin (DT) technology is a pivotal innovation within the built environment industry, facilitating digital transformation through advanced data integration and analytics. DTs have demonstrated significant benefits in building design, construction, and asset management, including optimising lifecycle energy use, enhancing operational efficiency, enabling predictive maintenance, and improving user adaptability. By integrating real-time data from IoT sensors with advanced analytics, DTs provide dynamic and actionable insights for better decision-making and resource management. Despite these promising benefits, several challenges impede the widespread adoption of DT technology, such as technological integration, data consistency, organisational adaptation, and cybersecurity concerns. Addressing these challenges requires interdisciplinary collaboration, standardisation of data formats, and the development of universal design and development platforms for DTs. This paper provides a comprehensive review of DT definitions, applications, capabilities, and challenges within the Architecture, Engineering, and Construction (AEC) industries. This paper provides important insights for researchers and professionals, helping them gain a more comprehensive and detailed view of DT. The findings also demonstrate the significant impact that DTs can have on this sector, contributing to advancing DT implementations and promoting sustainable and efficient building management practices. Ultimately, DT technology is set to revolutionise the AEC industries by enabling autonomous, data-driven decision-making and optimising building operations for enhanced productivity and performance.

The inherent nature of public health emergencies often renders them unpredictable and inadequately managed in a timely manner, resulting in significant impacts on lives, property, and social stability. In recent years, the frequency and severity of such emergencies have escalated. As an integration of artificial intelligence, machine learning, deep learning, big data, and the Internet of Things, digital twin technology has been increasingly adopted across diverse fields, including industrial production, urban planning, and healthcare. This study aimed to investigate recent advancements in digital twin technology, particularly in the fields of medicine and urban construction, with a focus on its potential to mitigate the impacts of public health crises. By employing the PPRR framework, this study further explored the prospects for constructing and applying digital twin technology within the public health management system. Taking perceived fidelity (SF), model plasticity (MP), decision coupling (DC), and governance credibility (GC) as the core constructs, we discussed the operability of unified modeling and quantitative evaluation of each stage of digital twin implementation. This approach comprehensively explained all four phases of emergency management: prevention, preparedness, response, and recovery. Through enabling the digitalization, intelligence, and visualization of the entire health emergency management process, digital twin technology offers holistic solutions throughout the life cycle of public health emergencies. These include preventive measures, real-time detection, early warning systems, and intelligent emergency response management. Such innovations have the potential to significantly enhance management efficiency and effectiveness, bolster coordinated responses to public health crises, and improve the overall resilience of public health systems.

This chapter presents digital twinning technology, integrating multi-disciplinary, multi-physical, multi-scale, and multi-probability simulation processes through physical models and sensors. It achieves complete mapping in virtual space, capturing the entire life cycle of corresponding entities. The study analyzes existing models in related fields, examining their uses and implementation methods. Applying digital twin technology to the power system, the chapter explores optimization design for the power grid, simulation of power grid faults, virtual power plants, intelligent equipment monitoring, and other services. The application scenario of power system digital twin (PSDT) is detailed. The chapter also explains the model structure of PSDT and the method of model reduction.