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104 Research products, page 1 of 11

  • Publications
  • 2013-2022
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  • DK
  • Transport Research

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  • Publication . Contribution for newspaper or weekly magazine . Conference object . 2022
    Open Access
    Authors: 
    Aidan Bowen; Jan Engelhardt; Tatiana Gabderakhmanova; Mattia Marinelli; Gunnar Rohde;
    Publisher: IEEE
    Country: Denmark

    The widespread adoption of electric vehicles (EVs) is a vital step in the reduction of emissions within the transport sector. However, the development of public fast charging infrastructure and the proper modeling of EV charging behaviours is required to enable this adoption. This paper presents charging data and patterns observed at a battery buffered fast charging DC microgrid on the Danish island of Bornholm. The charging sessions observed at this single site tend to be shorter with lower total energy transfer compared to studies with a wider scope. An atypical uptake of charges with higher than average energy transfer late in the evening is also observed. A simulation based study using this charging data to examine the effectiveness of the battery buffers at facilitating EV fast charging at reduced grid capacities is then presented. This study shows that the Bornholm DC microgrid would have been able to supply all observed EV charging at a reduced grid capacity of 11 kW, enabling such a system to provide EV fast charging at a much wider range of locations.

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2022
    Open Access
    Authors: 
    Kristian Sevdari; Simone Striani; Peter Bach Andersen; Mattia Marinelli;
    Publisher: IEEE
    Country: Denmark

    De-coupling transport sector from the use of petroleum is giving way to the rise of electric mobility. As compromising the user’s comfort is not an option managing the power system becomes a tall challenge, especially during peak hours. Thus, having a smart connection to the power system, such as an electric vehicle (EV) smart charger, is considered part of the solution. This paper focuses on assessing the capabilities of smart chargers in the context of helping the electrical network without compromising the user’s comfort. By using a Tesla Model S P85, Renault Zoe, and Nissan LEAF, the paper first evaluates differently controlled (centralized and distributed) smart chargers against the IEC 61851 standard. Second, it tests smart features such as peak-shaving, valley-filling, and phase balancing. Being representatives of the state-of-the-art, both chargers exceed standard requirements and offer new grid service possibilities. However, the bottleneck for providing faster grid services remains the EV on-board charger. The results from this article can help to better simulate the dynamic charging behaviors of EVs.

  • Open Access
    Authors: 
    Amandine Godet; Saber, J. T.; Nurup, J. N.; George Panagakos; Michael Bruhn Barfod;
    Country: Denmark

    In recent years, international shipping has received considerable attention with regard to reducing its greenhouse gas (GHG) emissions. While efficient ships are key, benchmarking the energy efficiency of ships is not straightforward. Technical indicators, such as the EEDI (Energy Efficiency Design Index), reflect a ship's efficiency in ideal conditions (calm sea, no wind, fully laden, design speed). In contrast, operational indicators, such as the EEOI (Energy Efficiency Operational Index), are affected by factors either completely out of the operator's control (weather conditions, etc.) or partially controllable due to market conditions (volume of cargo, speed, etc.). In its way towards decarbonization, the maritime industry needs a realistic benchmarking tool for ship energy efficiency that considers both technical and operational aspects. The automotive industry has been using driving cycles for decades to test and assess the efficiency of vehicles in terms of air pollutants, and more recently, GHG emissions. This concept does not exist in maritime transport, at least not in formal policy-making. This work investigates the possibility of applying the concept of operational cycles in the maritime industry based on experiences acquired from the automotive driving cycles. More specifically, we will: (i) present the motivations for developing operational cycles for ships, (ii) provide an overview of the methods and uses of the driving cycles in road transport, and (iii) suggest an initial procedure for developing these cycles in maritime transport, including the data needed. A literature review identifies the development and use of the driving cycles, the methodologies applied worldwide, and the benefits and limitations of the different types of driving cycles. We also identify the few applications of operational cycles in the maritime industry. The lessons learned from the automotive industry form the foundation for discussing the possibility of applying this concept in the maritime sector, considering the differences between the two industries. We identify the necessary data, and we discuss further development work along with the potential use of these cycles as a tool for enhancing policy-making and ultimately improving the design of efficient ships.

  • Open Access
    Authors: 
    Roberto Pili; Søren Bojer Jørgensen; Fredrik Haglind;
    Publisher: ECOS 2021 Program Organizers
    Country: Denmark
    Project: EC | EuroTechPostdoc (754462)

    The Organic Rankine cycle system is a well-established technology for converting medium/low temperature waste heat into mechanical or electrical power. Inefficiencies in the internal combustion engines for road transportation lead to large amounts of waste heat that are not exploited. Because of the engine load changes during a driving cycle, the mass flow rate and temperature of the heat source fluctuate rapidly over a broad range. This poses high requirements to the control of the organic Rankine cycle unit, in order to prevent the formation of liquid droplets at the turbine inlet and acid gas corrosion in the evaporator if the exhaust gas temperatures are too low, which reduce the system lifetime. In addition, the fluctuations in the heat source degrade the efficiency of the organic Rankine cycle unit, because of part-load operation. Furthermore, the penalty on the transportable vehicle payload caused by the increase in system mass should be considered. This paper presents a novel design method for organic Rankine cycle systems subject to highly fluctuating heat sources, ensuring safe and efficient operation. An integral optimization code developed in MATLAB®/Simulink® combining the design of the thermodynamic cycle, the system evaporator and the control system with a dynamic simulation model is presented. The multi-objective optimization maximizes the organic Rankine cycle net power output over a driving cycle of a heavy-duty truck, while minimizing the mass of the evaporator. The results indicate that, in order to ensure safe operation, the degree of superheating of the working fluid as well as the exhaust gas temperature leaving the evaporator at design conditions should be higher than what classical steady-state thermodynamic analyses suggest. This work provides a unique benchmark for the optimization of organic Rankine cycle systems subject to high fluctuating heat sources that will be of benefit both for academia and industry.

  • Open Access
    Authors: 
    Anders Fjendbo Jensen; Mikkel Thorhauge; Stefan Eriksen Mabit; Jeppe Rich;
    Publisher: Elsevier BV
    Country: Denmark

    The electrification of transport systems requires a change in the composition of the vehicle fleet towards higher shares of electric vehicles. A successful transition, however, depends on many factors of which some relate to purchase prices and vehicle features, while others relate to technology and charging infrastructure. This paper analyses the transition towards plug-in electric vehicles. We use data from a large representative Danish stated choice survey. Based on these data, we estimate a mixed logit model that allows for correlated random effects across fuel types and car segments as well as systematic heterogeneity. The results show that correlation and substitution indeed goes across these dimensions. Willingness-to-pay (WTP) measures are estimated for a variety of attributes. These suggest that the WTP for range varies with fuel types, that the possibility for home charging is highly valued, and that CO2 is a significant concern among individuals in the sample.

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2021
    Open Access English
    Authors: 
    Panagiotis Tsagkaroulis; Andreas Thingvad; Mattia Marinelli; Kenta Suzuki;
    Publisher: IEEE
    Country: Denmark

    Electric Vehicles (EV)s can with the right charger and aggregated in large numbers be considered as a large storage unit. If the chargers have bidirectional power converters, EVs connected to the grid, could provide Vehicle-to-Grid (V2G) ancillary services. The annual earnings from delivering frequency-controlled normal operation reserve in Denmark is calculated, based on individual user profiles. The individual earnings are strongly dependant on the driving time, distance and parking time at different locations which determine the availability of each EV to provide ancillary services. The specific user behaviour is based on a set of telematics data acquired from 7,163 Nissan LEAFs in the United States, with information about every driving and charging sessions during one year. The profit of the individual EV, spreads from 51 to 1654 AC/year. A data set of one year of system frequency measurements from the Nordic grid is used to calculate the impact of the service provision on the State of charge (SOC).

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2021
    Closed Access English
    Authors: 
    Reza Nasirigerdeh; Reihaneh Torkzadehmahani; Jan Baumbach; David Blumenthal;
    Publisher: Association for Computing Machinery
    Country: Denmark

    Federated learning (FL) is becoming an increasingly popular machine learning paradigm in application scenarios where sensitive data available at various local sites cannot be shared due to privacy protection regulations. In FL, the sensitive data never leaves the local sites and only model parameters are shared with a global aggregator. Nonetheless, it has recently been shown that, under some circumstances, the private data can be reconstructed from the model parameters, which implies that data leakage can occur in FL. In this paper, we draw attention to another risk associated with FL: Even if federated algorithms are individually privacy-preserving, combining them into pipelines is not necessarily privacy-preserving. We provide a concrete example from genome-wide association studies, where the combination of federated principal component analysis and federated linear regression allows the aggregator to retrieve sensitive patient data by solving an instance of the multidimensional subset sum problem. This supports the increasing awareness in the field that, for FL to be truly privacy-preserving, measures have to be undertaken to protect against data leakage at the aggregator.

  • Closed Access
    Authors: 
    Enrica Raheli; Qiuwei Wu; Changyun Wen;
    Publisher: IEEE

    The power system and the natural gas network are becoming increasingly interconnected due to the rising number of gas-fired power plants and the emergence of power-to-gas technology. The coordinated operation of power and gas systems is a promising solution to add flexibility to future energy systems, facilitating renewable integration. The optimal operation of the power system is commonly modeled as a mixed-integer linear problem (MILP). Conversely, the natural gas network optimization is a mixed-integer nonlinear problem (MINLP), due to the highly nonlinear and nonconvex Weymouth equation modeling the gas flow in pipelines. Different linearization and convexification approaches have been investigated in the literature for the gas optimization problem, but few authors have verified the quality of the solution. In this paper, a MILP model for the gas problem is developed using an outer approximation technique, and the feasibility of the solution is assessed. A sensitivity analysis on the number of linearization breakpoints is conducted to show the impact on the solution quality and computational time.

  • Closed Access
    Authors: 
    Pınar Kaygan; HARUN KAYGAN; Asuman Özgür;
    Country: Denmark

    The social construction of gender through the design of technological artefacts, such as automobiles, motorcycles and domestic technologies, has received growing interest within feminist technology studies (FTS). Building on the extant FTS literature, in this research we explore how design of public transport (bus, minibus, metro) as a sociotechnical system shapes women's experiences of commute in their everyday lives. Drawing on empirical data that comes from interviews with 32 women, we focus on the complex entanglements of the women’s interactions (1) within the vehicle as a technological artefact with its layout, interior elements and technologies such as cameras, and (2) with other passengers (both men and women) and the driver. These entanglements constitute gendered experiences in public transport. Our findings specify the strategies women develop with concerns of (physical and social) personal space, safety, and travel hours in public transport; some of which have gained more prominence during the Covid-19 pandemic. We underline the diversity of these strategies depending on vehicle types, routes, and time of travel within which women negotiate the material and social interactions. We argue that such interactions can, and should, inspire all stakeholders for responsible innovation for inclusive and egalitarian public transport design.

  • Closed Access
    Authors: 
    Vajira Thambawita; Steven Alexander Hicks; Jonas L. Isaksen; Mette Haug Stensen; Trine B. Haugen; Jørgen K. Kanters; Sravanthi Parasa; Thomas de Lange; Håvard D. Johansen; Dag Johansen; +3 more
    Publisher: IEEE

    Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.

Advanced search in
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
arrow_drop_down
Include:
104 Research products, page 1 of 11
  • Publication . Contribution for newspaper or weekly magazine . Conference object . 2022
    Open Access
    Authors: 
    Aidan Bowen; Jan Engelhardt; Tatiana Gabderakhmanova; Mattia Marinelli; Gunnar Rohde;
    Publisher: IEEE
    Country: Denmark

    The widespread adoption of electric vehicles (EVs) is a vital step in the reduction of emissions within the transport sector. However, the development of public fast charging infrastructure and the proper modeling of EV charging behaviours is required to enable this adoption. This paper presents charging data and patterns observed at a battery buffered fast charging DC microgrid on the Danish island of Bornholm. The charging sessions observed at this single site tend to be shorter with lower total energy transfer compared to studies with a wider scope. An atypical uptake of charges with higher than average energy transfer late in the evening is also observed. A simulation based study using this charging data to examine the effectiveness of the battery buffers at facilitating EV fast charging at reduced grid capacities is then presented. This study shows that the Bornholm DC microgrid would have been able to supply all observed EV charging at a reduced grid capacity of 11 kW, enabling such a system to provide EV fast charging at a much wider range of locations.

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2022
    Open Access
    Authors: 
    Kristian Sevdari; Simone Striani; Peter Bach Andersen; Mattia Marinelli;
    Publisher: IEEE
    Country: Denmark

    De-coupling transport sector from the use of petroleum is giving way to the rise of electric mobility. As compromising the user’s comfort is not an option managing the power system becomes a tall challenge, especially during peak hours. Thus, having a smart connection to the power system, such as an electric vehicle (EV) smart charger, is considered part of the solution. This paper focuses on assessing the capabilities of smart chargers in the context of helping the electrical network without compromising the user’s comfort. By using a Tesla Model S P85, Renault Zoe, and Nissan LEAF, the paper first evaluates differently controlled (centralized and distributed) smart chargers against the IEC 61851 standard. Second, it tests smart features such as peak-shaving, valley-filling, and phase balancing. Being representatives of the state-of-the-art, both chargers exceed standard requirements and offer new grid service possibilities. However, the bottleneck for providing faster grid services remains the EV on-board charger. The results from this article can help to better simulate the dynamic charging behaviors of EVs.

  • Open Access
    Authors: 
    Amandine Godet; Saber, J. T.; Nurup, J. N.; George Panagakos; Michael Bruhn Barfod;
    Country: Denmark

    In recent years, international shipping has received considerable attention with regard to reducing its greenhouse gas (GHG) emissions. While efficient ships are key, benchmarking the energy efficiency of ships is not straightforward. Technical indicators, such as the EEDI (Energy Efficiency Design Index), reflect a ship's efficiency in ideal conditions (calm sea, no wind, fully laden, design speed). In contrast, operational indicators, such as the EEOI (Energy Efficiency Operational Index), are affected by factors either completely out of the operator's control (weather conditions, etc.) or partially controllable due to market conditions (volume of cargo, speed, etc.). In its way towards decarbonization, the maritime industry needs a realistic benchmarking tool for ship energy efficiency that considers both technical and operational aspects. The automotive industry has been using driving cycles for decades to test and assess the efficiency of vehicles in terms of air pollutants, and more recently, GHG emissions. This concept does not exist in maritime transport, at least not in formal policy-making. This work investigates the possibility of applying the concept of operational cycles in the maritime industry based on experiences acquired from the automotive driving cycles. More specifically, we will: (i) present the motivations for developing operational cycles for ships, (ii) provide an overview of the methods and uses of the driving cycles in road transport, and (iii) suggest an initial procedure for developing these cycles in maritime transport, including the data needed. A literature review identifies the development and use of the driving cycles, the methodologies applied worldwide, and the benefits and limitations of the different types of driving cycles. We also identify the few applications of operational cycles in the maritime industry. The lessons learned from the automotive industry form the foundation for discussing the possibility of applying this concept in the maritime sector, considering the differences between the two industries. We identify the necessary data, and we discuss further development work along with the potential use of these cycles as a tool for enhancing policy-making and ultimately improving the design of efficient ships.

  • Open Access
    Authors: 
    Roberto Pili; Søren Bojer Jørgensen; Fredrik Haglind;
    Publisher: ECOS 2021 Program Organizers
    Country: Denmark
    Project: EC | EuroTechPostdoc (754462)

    The Organic Rankine cycle system is a well-established technology for converting medium/low temperature waste heat into mechanical or electrical power. Inefficiencies in the internal combustion engines for road transportation lead to large amounts of waste heat that are not exploited. Because of the engine load changes during a driving cycle, the mass flow rate and temperature of the heat source fluctuate rapidly over a broad range. This poses high requirements to the control of the organic Rankine cycle unit, in order to prevent the formation of liquid droplets at the turbine inlet and acid gas corrosion in the evaporator if the exhaust gas temperatures are too low, which reduce the system lifetime. In addition, the fluctuations in the heat source degrade the efficiency of the organic Rankine cycle unit, because of part-load operation. Furthermore, the penalty on the transportable vehicle payload caused by the increase in system mass should be considered. This paper presents a novel design method for organic Rankine cycle systems subject to highly fluctuating heat sources, ensuring safe and efficient operation. An integral optimization code developed in MATLAB®/Simulink® combining the design of the thermodynamic cycle, the system evaporator and the control system with a dynamic simulation model is presented. The multi-objective optimization maximizes the organic Rankine cycle net power output over a driving cycle of a heavy-duty truck, while minimizing the mass of the evaporator. The results indicate that, in order to ensure safe operation, the degree of superheating of the working fluid as well as the exhaust gas temperature leaving the evaporator at design conditions should be higher than what classical steady-state thermodynamic analyses suggest. This work provides a unique benchmark for the optimization of organic Rankine cycle systems subject to high fluctuating heat sources that will be of benefit both for academia and industry.

  • Open Access
    Authors: 
    Anders Fjendbo Jensen; Mikkel Thorhauge; Stefan Eriksen Mabit; Jeppe Rich;
    Publisher: Elsevier BV
    Country: Denmark

    The electrification of transport systems requires a change in the composition of the vehicle fleet towards higher shares of electric vehicles. A successful transition, however, depends on many factors of which some relate to purchase prices and vehicle features, while others relate to technology and charging infrastructure. This paper analyses the transition towards plug-in electric vehicles. We use data from a large representative Danish stated choice survey. Based on these data, we estimate a mixed logit model that allows for correlated random effects across fuel types and car segments as well as systematic heterogeneity. The results show that correlation and substitution indeed goes across these dimensions. Willingness-to-pay (WTP) measures are estimated for a variety of attributes. These suggest that the WTP for range varies with fuel types, that the possibility for home charging is highly valued, and that CO2 is a significant concern among individuals in the sample.

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2021
    Open Access English
    Authors: 
    Panagiotis Tsagkaroulis; Andreas Thingvad; Mattia Marinelli; Kenta Suzuki;
    Publisher: IEEE
    Country: Denmark

    Electric Vehicles (EV)s can with the right charger and aggregated in large numbers be considered as a large storage unit. If the chargers have bidirectional power converters, EVs connected to the grid, could provide Vehicle-to-Grid (V2G) ancillary services. The annual earnings from delivering frequency-controlled normal operation reserve in Denmark is calculated, based on individual user profiles. The individual earnings are strongly dependant on the driving time, distance and parking time at different locations which determine the availability of each EV to provide ancillary services. The specific user behaviour is based on a set of telematics data acquired from 7,163 Nissan LEAFs in the United States, with information about every driving and charging sessions during one year. The profit of the individual EV, spreads from 51 to 1654 AC/year. A data set of one year of system frequency measurements from the Nordic grid is used to calculate the impact of the service provision on the State of charge (SOC).

  • Publication . Conference object . Contribution for newspaper or weekly magazine . 2021
    Closed Access English
    Authors: 
    Reza Nasirigerdeh; Reihaneh Torkzadehmahani; Jan Baumbach; David Blumenthal;
    Publisher: Association for Computing Machinery
    Country: Denmark

    Federated learning (FL) is becoming an increasingly popular machine learning paradigm in application scenarios where sensitive data available at various local sites cannot be shared due to privacy protection regulations. In FL, the sensitive data never leaves the local sites and only model parameters are shared with a global aggregator. Nonetheless, it has recently been shown that, under some circumstances, the private data can be reconstructed from the model parameters, which implies that data leakage can occur in FL. In this paper, we draw attention to another risk associated with FL: Even if federated algorithms are individually privacy-preserving, combining them into pipelines is not necessarily privacy-preserving. We provide a concrete example from genome-wide association studies, where the combination of federated principal component analysis and federated linear regression allows the aggregator to retrieve sensitive patient data by solving an instance of the multidimensional subset sum problem. This supports the increasing awareness in the field that, for FL to be truly privacy-preserving, measures have to be undertaken to protect against data leakage at the aggregator.

  • Closed Access
    Authors: 
    Enrica Raheli; Qiuwei Wu; Changyun Wen;
    Publisher: IEEE

    The power system and the natural gas network are becoming increasingly interconnected due to the rising number of gas-fired power plants and the emergence of power-to-gas technology. The coordinated operation of power and gas systems is a promising solution to add flexibility to future energy systems, facilitating renewable integration. The optimal operation of the power system is commonly modeled as a mixed-integer linear problem (MILP). Conversely, the natural gas network optimization is a mixed-integer nonlinear problem (MINLP), due to the highly nonlinear and nonconvex Weymouth equation modeling the gas flow in pipelines. Different linearization and convexification approaches have been investigated in the literature for the gas optimization problem, but few authors have verified the quality of the solution. In this paper, a MILP model for the gas problem is developed using an outer approximation technique, and the feasibility of the solution is assessed. A sensitivity analysis on the number of linearization breakpoints is conducted to show the impact on the solution quality and computational time.

  • Closed Access
    Authors: 
    Pınar Kaygan; HARUN KAYGAN; Asuman Özgür;
    Country: Denmark

    The social construction of gender through the design of technological artefacts, such as automobiles, motorcycles and domestic technologies, has received growing interest within feminist technology studies (FTS). Building on the extant FTS literature, in this research we explore how design of public transport (bus, minibus, metro) as a sociotechnical system shapes women's experiences of commute in their everyday lives. Drawing on empirical data that comes from interviews with 32 women, we focus on the complex entanglements of the women’s interactions (1) within the vehicle as a technological artefact with its layout, interior elements and technologies such as cameras, and (2) with other passengers (both men and women) and the driver. These entanglements constitute gendered experiences in public transport. Our findings specify the strategies women develop with concerns of (physical and social) personal space, safety, and travel hours in public transport; some of which have gained more prominence during the Covid-19 pandemic. We underline the diversity of these strategies depending on vehicle types, routes, and time of travel within which women negotiate the material and social interactions. We argue that such interactions can, and should, inspire all stakeholders for responsible innovation for inclusive and egalitarian public transport design.

  • Closed Access
    Authors: 
    Vajira Thambawita; Steven Alexander Hicks; Jonas L. Isaksen; Mette Haug Stensen; Trine B. Haugen; Jørgen K. Kanters; Sravanthi Parasa; Thomas de Lange; Håvard D. Johansen; Dag Johansen; +3 more
    Publisher: IEEE

    Limited access to medical data is a barrier on developing new and efficient machine learning solutions in medicine such as computer-aided diagnosis, risk assessments, predicting optimal treatments and home-based personal healthcare systems. This paper presents DeepSynthBody: a novel framework that overcomes some of the inherent restrictions and limitations of medical data by using deep generative adversarial networks to produce synthetic data with characteristics similar to the real data, so-called DeepSynth (deep synthetic) data. We show that DeepSynthBody can address two key issues commonly associated with medical data, namely privacy concerns (as a result of data protection rules and regulations) and the high costs of annotations. To demonstrate the full pipeline of applying DeepSynthBody concepts and user-friendly functionalities, we also describe a synthetic medical dataset generated and published using our framework. DeepSynthBody opens a new era of machine learning applications in medicine with a synthetic model of the human body.

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