This paper presents a model specifically developed to explore the mobility impacts of connected and automated driving and shared mobility. It is an explorative iterative model that uses an elasticity model for destination choice, a multinomial logit model for mode choice and a network fundamental diagram to assess traffic impacts. To the best of the authors’ knowledge, it is the first model that combines a network fundamental diagram with choice models. A second contribution is the inclusion of automated vehicles, automated (shared) taxis, automated shared vans and new parking concepts in the model as well as the way in which they affect mobility choices and traffic conditions. The insights into the direct mobility impacts are the third contribution. The short computation time of the model enables exploration of large numbers of scenarios, sensitivity analyses and assessments of the impacts of interventions. The model was applied in a case study of the Dutch Province of North-Holland, in which the potential impacts of automated and shared vehicles and mitigating interventions were explored. In this case study, four extreme scenarios were explored, in which 100% of the vehicles have SAE-level 3/4 or 5 and people have a low or high willingness to share. The extremes were chosen to get insights into maximum effects. The results show that if automated vehicles and sharing are accepted, it is likely that there will be considerable changes in mobility patterns and traffic performance, with both positive and problematic effects.@en

This paper presents a model specifically developed to explore the mobility impacts of connected and automated driving and shared mobility. It is an explorative iterative model that uses an elasticity model for destination choice, a multinomial logit model for mode choice and a network fundamental diagram to assess traffic impacts. To the best of the authors’ knowledge, it is the first model that combines a network fundamental diagram with choice models. A second contribution is the inclusion of automated vehicles, automated (shared) taxis, automated shared vans and new parking concepts in the model as well as the way in which they affect mobility choices and traffic conditions. The insights into the direct mobility impacts are the third contribution. The short computation time of the model enables exploration of large numbers of scenarios, sensitivity analyses and assessments of the impacts of interventions. The model was applied in a case study of the Dutch Province of North-Holland, in which the potential impacts of automated and shared vehicles and mitigating interventions were explored. In this case study, four extreme scenarios were explored, in which 100% of the vehicles have SAE-level 3/4 or 5 and people have a low or high willingness to share. The extremes were chosen to get insights into maximum effects. The results show that if automated vehicles and sharing are accepted, it is likely that there will be considerable changes in mobility patterns and traffic performance, with both positive and problematic effects.@en

Abstract Portable electronic devices, such as laptop computers and personal portable electronic devices are battery-powered and need power-electronics converters as an interface between the battery and the load. To ensure a long battery life, it is important that the power-electronic converters operate at high efficiency. The volume available for power electronics is limited which necessitates the use of integration technologies to achieve high-power-density converters. Integrating passive components in silicon makes monolithic integration of power converters in system-on-chip difficult and hybrid integration in the form of system-in-package where passive components are implemented in alternative technologies and integrated in the package seems to be a more feasible option at present. High operating frequencies, necessary for the reduction of the passive components size results in the increase of switching losses in the power semiconductors which negatively influences the efficiency. Furthermore, power semiconductor losses are directly dependent on the chip size which in turn influences the cost of the converter. Also, the losses are dependent on the time waveforms of electrical currents and voltages which in turn depend on the chosen circuit topology. The paper presents a topology comparison and a design optimization procedure for the implementation of a DC-DC converter (Vin 3 V- 4.2 V from Li-ion battery, Vout 0.6V-1.2 V, load current@en