Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a Driver Digital Twin (DDT) for the online prediction of personalized lane change behavior, allowing CAVs to predict surrounding vehicles' behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge-cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the lane change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network. The lane change intention can be recognized in 6 seconds on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 meters within a 4-second prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM

Towards Transportation Digital Twin Systems for Traffic Safety and Mobility Applications: A Review

Digital twin (DT) systems aim to create virtual replicas of physical objects that are updated in real time with their physical counterparts and evolve alongside the physical assets throughout its lifecycle. Transportation systems are poised to significantly benefit from this new paradigm. In particular, DT technology can augment the capabilities of intelligent transportation systems. However, the development and deployment of networkwide transportation DT systems need to take into consideration the scale and dynamic nature of future connected and automated transportation systems. Motivated by the need of understanding the requirements and challenges involved in developing and implementing such systems, this paper proposes a hierarchical concept for a Transportation DT (TDT) system starting from individual transportation assets and building up to the entire networkwide TDT. A reference architecture is proposed for TDT systems that could be used as a guide in developing TDT systems at any scale within the presented hierarchical concept. In addition, several use cases are presented based upon the reference architecture which illustrate the utility of a TDT system from transportation safety, mobility and environmental applications perspective. This is followed by a review of current studies in the domain of TDT systems. Finally, the critical challenges and promising future research directions in TDT are discussed to overcome existing barriers to realize a safe and operationally efficient connected and automated transportation systems.Comment: 15 pages, 2 figures; corrected issue in author(s) fiel

Digital Twin for Real-time Li-ion Battery State of Health Estimation with Partially Discharged Cycling Data

To meet the fairly high safety and reliability requirements in practice, the state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a close relationship with the degradation performance, has been extensively studied with the widespread applications of various electronics. The conventional SOH estimation approaches with digital twin are end-of-cycle estimation that require the completion of a full charge/discharge cycle to observe the maximum available capacity. However, under dynamic operating conditions with partially discharged data, it is impossible to sense accurate real-time SOH estimation for LIBs. To bridge this research gap, we put forward a digital twin framework to gain the capability of sensing the battery's SOH on the fly, updating the physical battery model. The proposed digital twin solution consists of three core components to enable real-time SOH estimation without requiring a complete discharge. First, to handle the variable training cycling data, the energy discrepancy-aware cycling synchronization is proposed to align cycling data with guaranteeing the same data structure. Second, to explore the temporal importance of different training sampling times, a time-attention SOH estimation model is developed with data encoding to capture the degradation behavior over cycles, excluding adverse influences of unimportant samples. Finally, for online implementation, a similarity analysis-based data reconstruction has been put forward to provide real-time SOH estimation without requiring a full discharge cycle. Through a series of results conducted on a widely used benchmark, the proposed method yields the real-time SOH estimation with errors less than 1% for most sampling times in ongoing cycles.Comment: This paper has been accepted for IEEE Transactions on Industrial Informatic

Development of an EV powertrain on system level by utilizing simulation-based design platforms

The challenges within electric powertrain design are managing multiple physics, time scales and spatial scales. There are existing methods available in different industries for modeling individual functional blocks of the electric powertrain. In this thesis a system level model of an electric vehicle (EV) powertrain is developed by examining different modeling and simulation methods. The final applications of an electrified powertrain can be for instance tractors, dumpers, harvesters and passenger cars. The target of the study is to provide modeling methods for evaluating energy efficiency and the performance of an electric powertrain. We focus on modeling a system including the battery, electric machine and a load. This Master’s thesis is done for EDR & Medeso oy’s Digital Electrification Laboratory (DEL) co-innovation project, which is a part of the e3Power project funded by Business Finland, that investigates the electrification of vehicles. The modeling and simulation are done in a digital platform using Ansys simulation software. Turku University of Applied Sciences eRallycross car project is used as a public reference for the modeling and simulation of the electric powertrain. The thesis aims to divide the electric powertrain into functional blocks, analyze functional block models and to define generic functional block parameters for the implementation of a system representation. Developed models utilize actual physical measurements performed on the eRallycross car components. Electrical, thermal and mechanical performance of the electric powertrain is analyzed. The study shows that it is possible to model and simulate a complex system that includes multiple physics and fidelities. The fidelity of each component model is adjustable and highly dependent of the input values available. Parameter ranges can be defined for individual component models. A main challenge of the study was the lack of component information from the manufacturers side. The study represents the first trial of a development platform for modeling an electric powertrain on system level. During the DEL project the system model shall be further improved, so that the system model can enable reliability and efficiency improvements of existing electric powertrains, and structural or operational changes for future electric powertrain designs.Sähköisten voimansiirtoketjujen mallien haasteita ovat eri fysiikoiden, aika- ja tila-alueiden hallitseminen. Eri aloilla on vallitsevia menetelmiä yksittäisten sähköisten voimansiirtoketjujen komponenttien mallinnukseen. Tässä tutkimuksessa kehitetään järjestelmätason malli sähköisen ajoneuvon voimansiirtoketjusta tutkimalla eri mallinnus- ja simulointimenetelmiä. Sähköistetyn voimansiirtoketjun sovellusalue voi olla esimerkiksi traktorit, dumpperit, puimurit ja henkilöautot. Työn tavoite on tarjota mallinnusmenetelmiä sähköisen voimansiirtoketjun energiatehokkuuden ja suorituskyvyn arviointiin. Keskitymme mallintamaan järjestelmää, joka koostuu akustosta, sähkömoottorista ja kuormasta. Tämä diplomityö tehdään EDR & Medeso oy:n Digital Electrification Laboratory (DEL) projektille, joka on osa Business Finlandin rahoittamaa ePower projektia, jossa tutkitaan ajoneuvojen sähköistymistä. Mallinnus ja simulointi suoritetaan digitaalisella alustalla käyttäen Ansys − simulointiohjelmistoa. Turun ammattikorkeakoulun eRallycross projektia käytetään julkisena viitteenä sähköisen voimansiirtoketjun mallinnuksissa ja simuloinneissa. Tutkimus pyrkii jakamaan sähköisen voimansiirtoketjun komponenteiksi, analysoimaan komponenttien malleja ja määrittelemään geneerisiä komponenttien parametreja järjestelmän esityksen toteuttamiseen. Kehitetyt mallit hyödyntävät fyysisiä mittaustuloksia, jotka suoritetaan eRallycross auton komponenteille. Sähköisen voimansiirtoketjun sähköistä, termistä ja mekaanista suorituskykyä analysoidaan. Tutkimus osoittaa, että on mahdollista mallintaa ja simuloida monimutkainen järjestelmä, joka sisältää useampaa fysiikkaa sekä tarkkuustasoa. Jokaisen komponenttimallin tarkkuustaso on säädettävissä ja riippuvainen saatavilla olevista sisäänmenoarvoista. Parametrien vaihteluvälit voidaan määritellä yksittäisille komponenttimalleille. Tutkimuksen eräs haaste oli komponenttitietojen puute valmistajien puolelta. Tutkimus edustaa alustavaa suunnittelualustaa järjestelmätason sähköisen voimansiirtoketjun suunnitteluun. DEL projektin aikana järjestelmämallia parannetaan niin, että järjestelmämalli mahdollistaa parannuksia olemassa olevien sähköisten voimansiirtoketjujen luotettavuudessa ja tehokkuudessa, sekä rakenteellisia ja toiminnallisia muutoksia tulevilla sähköisillä voimansiirtoketjumalleilla.Hantering av multifysik, olika tids- och rumsskalor tillhör utmaningarna av planeringen av elektriska drivlinor. Inom olika industrier finns det metoder för modelleringen av komponenter som tillhör elektriska drivlinor. I denna forskning utvecklas en system modell av en elektrisk fordons drivlina genom att utforska olika metoder kring modellering och simulering. Tillämpningsområden för elektriska drivlinor kan vara till exempel traktorer, dumprar, skördare och personbilar. Målet med detta diplomarbete är att presentera modelleringsmetoder för validering av energieffektivitet och prestation av en elektrisk drivlina. Vi fokuserar på modellering av ett system som består av ett batteri, en elektrisk motor och en belastning. Detta diplomarbete görs för EDR & Medeso ab:s Digital Electrification Laboratory (DEL) projekt, som tillhör Business Finlands finansierade e3Power projekt där elektrifiering av fordon utforskas. Modellering och simulering utförs på en virtuell platform av Ansys. Åbo yrkeshögskolans eRallycross projekt används som en offentlig referens för modelleringen och simuleringen av den elektriska drivlinan. Arbetet strävar till att dela upp den elektriska drivlinan i komponenter, analysera komponenterna och identifiera generella parametrar för komponenterna för skapandet av en representation av systemet. Utvecklade modellerna utnyttjar fysiska mätningar som gjorts på eRallycross bilen. Elektriska, värme och mekaniska effekter av den elektriska drivlinan analyseras. Arbetet visar att det är möjligt att modellera och simulera ett komplext system, som inkluderar flera fysikområden och noggranhetsnivåer. Noggranhetsnivån för varje komponentmodell kan justeras och den är beroende av tillgängliga inputvärden. Räckvidden för parametrarna kan defineras för individuella komponentmodeller. En av arbetets utmaningar var bristen på komponent information från tillverkarens sida. Forskningen representerar ett basis av en utvecklingsplattform för modellering av en elektrisk drivlina på systemnivå. Under DEL projektet förbättras system modellen på så vis, att system modellen möjlighetgör förbättring av pålitlighet och effektivitet av elektriska drivlinor, och strukturella och funktionella förändringar för framtida modeller av elektriska drivlinor

Hybrid Turbo-Shaft Engine Digital Twinning for Autonomous Aircraft via AI and Synthetic Data Generation

Autonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital twins (DTs) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution based on AI, proper data management is essential in implementing DT as a service (DTaaS). One of the challenges in DT development is the availability of real-life data, particularly for training algorithms and verifying the functionality of DT. The current article focuses on data augmentation through synthetic data generation. This approach can facilitate the development of DT in case the developers do not have enough data to train the machine learning (ML) algorithm. The current twinning approach provides a prospective ideal state of the engine used for proactive monitoring of the engine’s health as an anomaly detection service. In line with the track of unmanned aircraft vehicles (UAVs) for urban air mobility in smart city applications, this paper focuses specifically on the common hybrid turbo-shaft in drones/helicopters. However, there is a significant gap in real-life similar synthetic data generation in the UAV domain literature. Therefore, rolling linear regression and Kalman filter algorithms were implemented on noise-added data, which simulate the data measured from the engine in a real-life operational life cycle. For both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters except the engine torque

Digital Twin Technology: A Review of Its Applications and Prominent Challenges

Digital twin is a virtual representation of physical product that is used as benchmark to evaluate, diagnose, optimize and supervise operational performance of products before venturing into mass full production in accordance with global standard. Digital twin merges virtual and physical objects together via sensors and IoT to transmit data and keep traces of objects interactivity within present environments. In virtual model environment, digital twin permits product troubleshooting and testing to minimize rate of failure and product defects during product manufacturing to enhance effectiveness and customers’ satisfaction. Digital twin is utilized throughout product life-cycle to simulate, optimize and predict product quality before final production is financed. Digital twin is beneficial to modern digital society because attitude of modern factory workers can be boosted to improve motivation to work. Digital twin has come to stay, future product suppliers may be required to put forward digital twin of their products beforehand for virtual lab testing before making order while suppliers that fail to comply may be left over. With emergence of digital twin, virtual testing can be conducted on proposed products before finding their ways into physical marketplaces. Business sector remains most beneficiaries of digital twin to predict present and future state of physical product via digital peer analysis. Today, digital twin application can support enterprises by improving product performances, decision making and customers’ satisfactions on logistic and operational workflow. However, in this survey of digital twin research, efforts have been made to review in detail about digital twin, its impact and benefits to modern society, its architecture; security challenges and how solutions are proffered. It is believed that ICT experts, manufacturers and industries will leverage on this research to improve QoS (Quality of Service) for new and future products to take full advantage of profits on investment returns via digital twin

Digital-Twins towards Cyber-Physical Systems: A Brief Survey

Cyber-Physical Systems (CPS) are integrations of computation and physical processes. Physical processes are monitored and controlled by embedded computers and networks, which frequently have feedback loops where physical processes affect computations and vice versa. To ease the analysis of a system, the costly physical plants can be replaced by the high-fidelity virtual models that provide a framework for Digital-Twins (DT). This paper aims to briefly review the state-of-the-art and recent developments in DT and CPS. Three main components in CPS, including communication, control, and computation, are reviewed. Besides, the main tools and methodologies required for implementing practical DT are discussed by following the main applications of DT in the fourth industrial revolution through aspects of smart manufacturing, sixth wireless generation (6G), health, production, energy, and so on. Finally, the main limitations and ideas for future remarks are talked about followed by a short guideline for real-world application of DT towards CPS

Digital Twins for Ports: Derived from Smart City and Supply Chain Twinning Experience

Ports are striving for innovative technological solutions to cope with the ever-increasing growth of transport, while at the same time improving their environmental footprint. An emerging technology that has the potential to substantially increase the efficiency of the multifaceted and interconnected port processes is the digital twin. Although digital twins have been successfully integrated in many industries, there is still a lack of cross-domain understanding of what constitutes a digital twin. Furthermore, the implementation of the digital twin in complex systems such as the port is still in its infancy. This paper attempts to fill this research gap by conducting an extensive cross-domain literature review of what constitutes a digital twin, keeping in mind the extent to which the respective findings can be applied to the port. It turns out that the digital twin of the port is most comparable to complex systems such as smart cities and supply chains, both in terms of its functional relevance as well as in terms of its requirements and characteristics. The conducted literature review, considering the different port processes and port characteristics, results in the identification of three core requirements of a digital port twin, which are described in detail. These include situational awareness, comprehensive data analytics capabilities for intelligent decision making, and the provision of an interface to promote multi-stakeholder governance and collaboration. Finally, specific operational scenarios are proposed on how the port's digital twin can contribute to energy savings by improving the use of port resources, facilities and operations.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Support for Technical Phases and Conceptual Model

Worldwide, many wheelchair users find it difficult to use or acquire a wheelchair that is appropriate for them, either because they do not have the necessary financial support or because they do not have access to trained healthcare professionals (HCPs), but they are essential for the correct provision of assistive products and user training. Consequently, although wheelchairs are designed to promote the well-being of many users, in many cases, they end up being abandoned or do not provide any benefit, with the chance of causing harm and potentially putting people in danger. This article proposes the creation and use of a Digital Twin (DT) of a Power Wheelchair (PWC) to promote the health of wheelchair users, by facilitating and improving the delivery of remote services by HCPs, as well as to include monitoring services to support timely maintenance. Specifically, a DT is a virtual counterpart that is seamlessly linked to a physical asset, both relying on data and information exchange for mirroring each other. Currently, DT is emerging and being applied to different areas as a promising approach to gather insightful data, which are shared between the physical and virtual worlds and facilitate the means to design, monitor, analyze, optimize, predict, and control physical entities. This article gives an overview of the Digital Twin concept, namely its definition, types, and properties, and seeks to synthesize the technologies and tools frequently used to enable Digital Twins; we also explain how a DT can be used in the technical phases of the PWC provision process and propose a conceptual model highlighting the use of an MDD approach benefiting from a Petri net formalism, which is presented to systematize the development of a PWC Dpublishersversionpublishe