Contributed by: Riccardo Ferrari, r.ferrari@tudelft.nl
PhD in Sparse Data Driven Methods for Prognosis of Electric Vehicles
The Delft Center for Systems and Control, at Delft University of Technology (The Netherlands), has one position open for a PhD in ”Sparse Data Driven Methods for Prognosis of Electric Vehicles”
The position is in collaboration with Volvo AB and will be hosted in Dr. Riccardo Ferrari’s group.
The introduction of next generation heavy electric vehicles, such as electric trucks, is seen as an important contribution to worldwide efforts to curb greenhouse gases emission levels. Still, to deliver their promised performances, such novel electric vehicles should be robust to faults and be designed to optimize their maintenance.
While advanced diagnosis and prognosis algorithms that are suitable for fleets of complex vehicles are model-based, their design, tuning and validation require considerable amounts of data. Large and densely populated data sets, unfortunately, may not always be available, especially during the design phase of such vehicles. The challenge of tuning and validating diagnosis and prognosis algorithms using datasets that are sparse over time and over the vehicles’ population is precisely the motivation for this PhD opening.
The successful candidate will carry out research as part of the project “SPARSITY: using data from sparse measurements for predictive maintenance”, which is an academic-industrial collaboration between Dr. Ferrari’s group at Delft Center for Systems and Control (TU Delft, The Netherlands) and Volvo Group, a world-leading automotive company headquartered in Gothenburg (Sweden).
Research topics will include, but will not be limited to:
adapting state-of-the-art system identification algorithms to use sparse datasets;
uncertainty quantification and propagation in complex nonlinear systems;
probabilistic methods for diagnosis and prognosis thresholds design and validation;
sensitivity analysis of diagnosis and prognosis performances with respect to data sparsity.
The resulting methodologies and algorithms will be tested against real use cases provided by Volvo, where
the candidates may spend a secondment period.
Requirements: We are looking for a strong and motivated applicant holding, or close to holding, a M.Sc. degree in a field related to the project, such as:
Systems & Control
Applied Mathematics
Mechanical engineering
Electrical or Electronics engineering
Vehicle engineering
Aerospace Engineering
A good command of the English language is required. Candidates with a background in fault diagnosis/prognosis, automotive electric powertrains or probabilistic methods such as Polynomial Chaos Expansion or Gaussian Process Regression are especially encouraged to apply.
Application procedure: Please apply before 15 February 2021 via https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details/?jobId=1793 and upload the following:
motivation letter (up to one page)
curriculum vitae;
list of publications, including citation count;
research statement (up to three pages);
transcripts of all exams taken and obtained degrees (in English);
names and contact information of two academic references (e.g. project/thesis supervisors);
up to 3 research-oriented documents (e.g. thesis, conference/journal publication)
A pre-employment screening can be part of the selection procedure. You can apply online. We will not process applications sent by email and/or post.
