Research on Automatic Driving Simulation Test System Based on Digital Twin

Purpose– Cloud Computing (CC) technology is getting implemented rapidly in the educational sector to improve learning, research and other administrative process. As evident from the literature review, most of these implementations are happening in the western countries such as USA, UK, while the level of implementation of CC in developing countries such as India is rare. Moreover, implementing CC technology in the educational sector require various decisions to be made by the managers of the Information Technology (IT) department such as selecting suitable deployment model, vendor providing cloud service, etc. in their respective university or institute. The purpose of this paper is to attempt to address one such decision. Since, different types of CC deployment are available; selecting a suitable one plays a key role, as it might have an impact on the requirements of various stakeholders such as students, teachers, administrative staff (especially the staff members in the IT department), etc. apart from affecting the overall performance of the facilities such as a laboratory. Naturally, a proper decision by analysing multiple perspectives has to be made while carrying out such strategic initiatives by any educational institute.Design/methodology/approach– A case study methodology has been chosen as the research methodology to discuss and demonstrate the above decision problem that was faced in real time by one of the educational institutes in India, offering high-quality management education. The IT managers of this institute were planning to switch over to CC technology for the computer laboratory and they have to make a decision of choosing suitable alternative CC deployment models such as private cloud (PRC), public cloud (PUC), community cloud (COC), hybrid cloud (HYC), etc. by analysing and comparing them based on various factors and perspectives such as elasticity, availability, scalability, etc. Since, multiple factors are involved in making such a strategic decision, the most commonly used Multi-Criteria Decision Making (MCDM) model – namely, the Analytic Hierarchy Process (AHP) is used as a decision support during the decision making process.Findings– The team of decision makers, who were planning to implement CC in the case institute, found that PRC is best as they believed that it would provide adequate cost savings, apart from providing necessary security to maintain confidential information such as student's detail, grades, etc.Research limitations/implications– The results obtained are based on a single case study. Hence, they cannot be generalized for institutions across educational sector. However, the decision making situation and understanding its impact on the stakeholders of the educational institute can be common across various educational institute.Practical implications– Using a real-life case study of an educational institute, this paper presented a strategic decision making situation, which needs to be considered by the IT managers of the educational institutes when they decide to switch over to CC technology. Various criteria to be considered during the decision making process was identified from the literature review were identified and enumerated. These factors would useful for the IT managers of the different educational institute and they can suitably add or delete these decision criteria as per their requirements and situation at hand. Moreover, the algorithm of AHP, which was used as a decision support, was presented in a step-by-step manner, which should be beneficial for the practitioners to apply the same for similar decision making situations.Originality/value– It is believed that this paper would be the first to report on a strategic decision of choosing the deployment model for CC technology especially in the educational sector. Similarly, this paper would also contribute to the field of CC, as it lists out the decision criteria that are to be considered for making the above decision, which has not got adequate importance. Lastly, this paper is also unique in the realm of AHP because application for a decision problem in the field of CC especially in the educational sector is least reported.

• 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.

Cloud computing and big data: Technologies and applications

Cloud computing has become a generic umbrella term for the flexible delivery of IT resources—such as storage, computing power, software development platforms, and applications—as services over the Internet. The foremost innovation is that the IT infrastructure no longer lies with the user, breaking up the previously monolithic ownership and administrative control of its assets. The combination of cloud computing and semantic technologies holds great potential. In this chapter, we analyze three ways in which cloud computing and semantic technologies can be combined: (1) building on cloud computing technologies, e.g. from the area of distributed computing, to realize better semantic applications and enable semantic technologies to scale to ever larger data sets, (2) delivering semantic technologies as services in the cloud, and (3) using semantic technologies to improve cloud computing, in particular to further improve automatic data-center management. For each of these dimensions we identify challenges and opportunities, provide a survey, and present a research roadmap.

Only recently, smart cities are taking shape, thanks to the rapid development of Internet of Things (IoT), cloud computing, and other similar technologies. Given the high demands placed on advanced technologies such as autonomous driving, cloud data services, and high-precision sensors, smart cities are creating an intelligent transportation environment conducive to the introduction of autonomous vehicles (AVs). In this context, the use of AVs in transportation is also considered a form of transportation innovation. As a result, AVs are considered more favorable to people interested in new technologies because they appear to be technologically superior. Their association with the most up-to-date technology can serve as a symbol for those who wish to demonstrate their interest in new technologies through their appearance. The positive image of technological innovation projected by AVs may influence their acceptance among technology enthusiasts to a significant degree. In this context, this study investigates the effects of perceived advantage, perceived risk, and perceived safety on the intention to use autonomous vehicles. For this purpose, data were collected from vehicle users living in Turkey by survey method. Secondly, factor analyses and regression analyses were performed with the data set obtained from 611 participants. As a result of the analyses, it has been determined that the perceived advantage and perceived security increase the intention to use autonomous vehicles. In contrast, the perceived risk reduces this intention to use. According to these results, recommendations were made to the companies about the level of acceptance of this technology by the users to assess their investments in autonomous vehicles better.

Due to the rapid increase in the amount of data that people need to deal with computing tasks, such as distributed computing, cloud computing and other technologies are more and more concerned by most people. In recent years, with people's attention to information security and personal privacy protection, cloud computing security has gradually become the focus of researchers. In the cloud computing environment, massive distributed data storage and management and data centralization autonomy can control the data mechanism, and distribute management of redundant data and transaction processing. These management and application are generally through the distributed database for actual operation, from the user's point of view, the access process to the distributed database is transparent. In fact, from the user's point of view, a single distributed database is essentially a combination of a group of databases stored on multiple computers. This paper studies the distributed data processing system based on cloud computing technology.

With the acceleration of the construction of smart grids, the explosive growth of information brought about by weather, equipment, and electricity/gas/heat multi-energy scenarios in the power system has made it difficult for traditional power simulation systems to meet people’s needs for smart power grid construction. And the combination of machine learning, deep learning, cloud computing and digital twin technology in the Internet of Things has become the key to the current grid construction. This article first analyzes the application of digital twin technology in battery management, creates an intelligent battery operation and maintenance management and advanced measurement system based on the Internet of Things, and provides a certain reference for improving the detection level of battery operation and maintenance management systems.

Owing to the under-estimates risk control during the process of sea reclamation project, the value of digital twin technology application in sea reclamation project was investigated and risk control method of sea reclamation project based on digital twin technology was explored. Combining with big data, cloud computing and simulation technologies, digital twin risk management and control platform was established to investigate the framework and keys of digital twin technology application in sea reclamation project. According to a case study with practical project, numerical and stimulation analysis was conducted to investigate the risk during the steel cylinder transportation process. Result indicates that DT technology can significantly improve the risk management in the sea reclamation construction.

The paper proposes the method of building the electric power marketing business application system based on the cloud computing and microservices architecture technologies to solve the issues of the current system with the concentrated infrastructure and the monolithic architecture which are difficult to support the business processing in very large scale and the evolution of the electric power marketing business. The paper first introduces the current electric power marketing business application system and highlights its main drawbacks. Then it proposes the method to build the innovative electric power marketing business application system with the main technical characteristics as follows: the IaaS part of the application system has the distributed infrastructures with the IT resource elastic management and powerful horizontal scalability brought by the cloud computing technology; its PaaS part is the service platform with the components of the data processing, information integration, application building, and cloud service center to provide the several cloud services, such as one-click deployment, flexible scaling, fault self-healing, gray distribution, full-link monitoring and other cloud service capabilities; and its SaaS part has the microservices architecture with the features of servitization, componentization, decentralization, independent deployment etc. Finally, the suggestions are given to design the microservices for the electric power marketing business application.

Research on collaborative innovation of key common technologies in new energy vehicle industry based on digital twin technology

The advent of advanced computer architectures for parallel and symbolic processing has evolved to the point where the technology currently exists for the development of prototype autonomous vehicles. Control of such devices will require communication between knowledge-based subsystems in charge of the vision, planning, and conflict resolution aspects necessary to make autonomous vehicles functional in a real world environment. This paper describes a heuristic route planning system capable of forming the planning foundation of a autonomous ground vehicle. The route planner described herein is applicable to a variety of natural terrain ground systems such as autonomous tactical vehicles and mobile robot sentries. The route planner discussed consists of four processing stages: (1) scene recognition, (2) route planning, (3) scene matching (4) knowledge-based validation. Each of these stages is discussed and examples are provided where appropriate.

The advent of advanced computer architectures for parallel and symbolic processing has evolved to the point where the technology currently exists for the development of prototype autonomous vehicles. Control of such devices will require communication between knowledge-based subsystems in charge of the vision, planning, and conflict resolution aspects necessary to make autonomous vehicles functional in a real world environment. This paper describes a heuristic route planning system capable of forming the planning foundation of an autonomous ground vehicle.

Maritime shipping business has been an important event in global trade, but it is still marred by serious challenges such as high prices, environmental effects, hazards on ships and port congestion. With the use of technologies like Internet of Things, cloud computing, and artificial intelligence gaining an ever-increasing significance in the Industry 4.0 epoch, smarter and more efficient shipping solutions are getting recognized as an issue of even greater importance. In that case, the digital twin technology that is the development of virtual depictions of physical systems in real-time opportunity presents great potential to overcome these difficulties. The review is a collection of research on the topic carried out during the last five years to explore how digital twins are applied to smart ships, ports, and logistics systems and determine the main trends in technologies, application patterns, and gaps in research. It also addresses the way, in which digital twins can influence the forthcoming alteration of the maritime industry. Thus, the study of the digital twin technology in marine shipping is both theoretical and practical.

This study focuses on the cost control of construction projects under the general engineering contracting mode based on digital twin technology. Through the centralized procurement of Internet of Things, cloud computing, transportation, and storage optimization, and the digital management of bill of quantities, it is found that it can effectively optimize the procurement cost and reduce the loss. Combined with the actual project, the comprehensive unit price and total price differences of labor cost, material cost, and machinery cost under the optimization of digital twin technology are compared and analyzed, and the key influencing factors and optimization path are revealed. The study further proposes a dynamic monitoring mechanism. At the same time, based on the characteristics of the general engineering contracting (EPC) mode, it is suggested to strengthen the connection between pre-planning, process monitoring, and completion settlement from the whole process of design procurement, construction, big data cost control management, so as to improve the coordination efficiency. The case analysis shows that the EPC contracting mode can reduce the comprehensive cost by 10% -15% by integrating resources and optimizing the supply chain through digital twin technology, providing theoretical and practical support for construction enterprises to maximize economic benefits. The research results have reference value for improving the EPC project system and enhancing the anti-risk ability of the industry.

Today's autonomous vehicles are equipped with a variety of sensors, control techniques and actuators depending on the objectives and requirements of the addressing issue. In research, some even employ commercial cars to house their devices. The high cost of all these components represents a limitation in the development of an autonomous vehicle system. Therefore, researchers have no option but to rely on experimentation under simulated environments or to use small vehicles that in many cases do not accurately represent a real parametric dynamic system. This article describes the design of an autonomous electric vehicle prototype, which includes control systems, microcontrollers, encoders, motor controllers, LIDAR, and cameras, with the aim of having a reference in the literature that is low-cost and whose program code is employable in a real-life context. The present vehicle, whose structure is based on a conventional tubular go-kart, has been provided with the capability of moving autonomously by using computer vision and artificial intelligence algorithms. This document records the first steps of the design taking into account parameters of a real-life environment and the flexibility of A.I. algorithms.