Efficient Parameterization



The need for CO2 reduction has resulted in increased electrification, lightweight construction design and advanced predictive energy and thermal control strategies based on high-fidelity models, e.g. detailed battery models and complex cooling systems for passengers and vehicle components, as well as elaborate models of novel materials and material structures. High-fidelity models, such as Partial Differential Equations (PDEs), often depend on uncertain input parameters (fields), where correct parameterization is difficult and under certain circumstances ambiguous or not possible. Hence, dependable parameterization is paramount for validation and paths the way towards virtual approval.

Objectives/Tender Information

  •  State of the art study, recent trends in battery cell modelling and parameterization for automotive applications
  •  High-dimensional parameter spaces may initially be reduced by methods of sensitivity analysis. “Randomized Methods” offer a chance to use stochastic methods to cope with high dimensionality
  • Stochastic optimization methods and PDE-restricted optimization
  • Devising a novel optimal experimental design strategy as an inverse problem governed by forward PDE models with uncertain parameters
  • Development of an infinite-dimensional optimization system for devising excitation signals with maximum yield with respect to parameter uncertainty
  • Derive convergence statements for the system devised

In the proposal for PhD funding, scientific institutions describe:

  • The scientific ability and (if necessary) the required infrastructure to scientifically guide and supervise the PhD candidate
  • The research plan and the focus for the advertised thesis
  • The publication and dissemination plan
  • The teaching plan (courses, trainings, etc.) for the doctoral studies
  • Planned supporting master’s and bachelor theses
  • Total costs of the PhD thesis as well as requested total amount of financial contribution from VIRTUAL VEHICLE (note: adequacy of costs is a major decision criterion)
  • The application comprises the scientific CV of the PhD candidate and the supervising professor at the scientific institution

Please use our online contact form to send us your application.