Frequency control reserve provision from a fleet of shared autonomous electric vehicles - Iacobucci, Riccardo; Donhauser, Jonas; Schmöcker, Jan Dirk; Pruckner, Marco (2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2021, 2021)
Shared autonomous electric vehicles (SAEV) are expected to become an important transportation mode in cities within this decade. As part of the electrification of the transport system, they will increase the load on the power grid. However, thanks to their centralized control, these vehicles can also provide services to the grid, such as demand response and ancillary services. In this work, we explore this possibility by expanding a SAEV simulation and optimization framework with the inclusion of frequency control reserve (FCR) provision. To test the model with realistic transport patterns, we develop a stochastic transport demand model to generate trip data for an urban area based on a large transport survey of Germany. We tested our model with a large generated trip demand over a week and with real historical frequency and electricity price data for Germany. Our results show that SAEV fleets can provide FCR power without significant effects on transport quality. The potential added revenues may actually lead to larger fleets and better waiting times for passengers.
CRIS-Team Scopus Importer:2021-10-08

Neural network-based estimation of biomechanical vocal fold parameters - Donhauser, Jonas; Tur, Bogac; Döllinger, Michael (Frontiers in Physiology, 2024)
Vocal fold (VF) vibrations are the primary source of human phonation. High-speed video (HSV) endoscopy enables the computation of descriptive VF parameters for assessment of physiological properties of laryngeal dynamics, i.e., the vibration of the VFs. However, underlying biomechanical factors responsible for physiological and disordered VF vibrations cannot be accessed. In contrast, physically based numerical VF models reveal insights into the organ’s oscillations, which remain inaccessible through endoscopy. To estimate biomechanical properties, previous research has fitted subglottal pressure-driven mass–spring–damper systems, as inverse problem to the HSV-recorded VF trajectories, by global optimization of the numerical model. A neural network trained on the numerical model may be used as a substitute for computationally expensive optimization, yielding a fast evaluating surrogate of the biomechanical inverse problem. This paper proposes a convolutional recurrent neural network (CRNN)-based architecture trained on regression of a physiological-based biomechanical six-mass model (6 MM). To compare with previous research, the underlying biomechanical factor “subglottal pressure” prediction was tested against 288 HSV ex vivo porcine recordings. The contributions of this work are two-fold: first, the presented CRNN with the 6 MM handles multiple trajectories along the VFs, which allows for investigations on local changes in VF characteristics. Second, the network was trained to reproduce further important biomechanical model parameters like VF mass and stiffness on synthetic data. Unlike in a previous work, the network in this study is therefore an entire surrogate of the inverse problem, which allowed for explicit computation of the fitted model using our approach. The presented approach achieves a best-case mean absolute error (MAE) of 133 Pa (13.9%) in subglottal pressure prediction with 76.6% correlation on experimental data and a re-estimated fundamental frequency MAE of 15.9 Hz (9.9%). In-detail training analysis revealed subglottal pressure as the most learnable parameter. With the physiological-based model design and advances in fast parameter prediction, this work is a next step in biomechanical VF model fitting and the estimation of laryngeal kinematics.
CRIS-Team Scopus Importer:2024-03-15
Keywords: convolutional recurrent neural network; high-speed video; mass–spring–damper system; vocal fold dynamics; voice physiology

The demand potential of shared autonomous vehicles: a large-scale simulation using mobility survey data - Iacobucci, Riccardo; Donhauser, Jonas; Schmocker, Jan-Dirk; Pruckner, Marco (Journal of Intelligent Transportation Systems, 2023)
Shared Autonomous Vehicles (SAV), or robotaxis, are expected to be commercially available within this decade. This new transport mode has the potential to revolutionize travel, offering a level of service comparable to traditional taxis with much lower prices. This may attract travelers currently using other modes, impacting the economic sustainability of public transport as well as car ownership levels. We investigate this potential demand using a scalable SAV simulation framework. We do not establish a future equilibrium considering the interaction between all users on a detailed road network, but establish the potential demand for a large metropolitan area. Travelers can choose between their current mode and the new SAV mode, with fare and waiting times which depend on real-time demand. For our input data we train a statistical model on a large transport survey from Germany for an urban region, allowing us to generate a large number of trips with realistic characteristics. We conduct a sensitivity analysis to study the effect of several key parameters on the modal shift. We find that SAVs can be attractive to many active mode and public transport users unless regulations are put in place. Our results also show that due to SAV fleet constraints, changes in incentives for travelers currently using cars may have significant consequences on the behavior of other travelers. We further calculate key economic indicators for the fleet, which can inform the discussion on the fleet size and fare level that operators are likely to choose when maximizing their own profit.
CRIS-Team Scopus Importer:2023-05-26
Keywords: Autonomous vehicles; Bayesian network; mobility survey; mode shift; ride-hailing; simulation