Internship | Non-intrusive model identification of a physics-based battery model

Are you interested in contributing to a more sustainable future? In the powertrains department, we work to develop modern means of transport such that they meet strict gas regulations and contribute to a greener future.



Education type

university (wo)


Internship and graduation project

Hours a week

Fulltime – 40


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What will you be doing?

Electric Vehicles (EVs) are increasingly becoming an alternative for road transportation due to their potential environmental benefits and the depletion of carbon-based fuels resources. However, a wider application of such EVs still needs to overcome several technological barriers, many of them given by limitations in battery technology.
Batteries are the main source of energy of an EV and it is also the most expensive component. Therefore, strategies that lead to extended the battery life (i.e., less ageing) can have a positive effect in a wider implementation of EVs.

One of the strategies that can be used to extend the battery life, consist of improving modelling techniques such that the internal degradation processes of the batteries are better understood. One strategy consist of using battery models based on physics-based phenomena’s. These models can describe internal battery states (such as potentials, and concentrations), which are not measurable with sensors. 
However, identifying the model parameters is not a trivial task because of the equations complexity and limited input/output sensor information. 

The purpose of this assignment is to analyse, combine and implement the benefits of state-of-the-art techniques for non-intrusive model identification techniques for battery systems. The assignment covers the following tasks: 
  • Familiarize with physics-based models of batteries.
  • Critical state-of-the-art revision of non-intrusive identification techniques.
  • Identifiability analysis of the model parameters using current and voltage data. 
  • Design and implementation of an algorithm to identify model parameters with multiple experiments. 
  • Algorithm testing with simulation data and (if time allows) with measured data from battery cells.
  • Reporting writing.
  • Final presentation to colleagues. 

This assignment can be divided in two parts, where the first part is carried out as an internship and the second part as a master thesis. 

What do we require of you?

  • You have a bachelor’s degree in a relevant field, for example, electrical or chemical engineering. 
  • You are coursing a master in a relevant field such as control systems.
  • You have broad experience in Matlab/Simulink. 
  • You have previous experiences/courses with control systems, especially related to optimization and modelling techniques.
  • Modelling of chemical processes is desired but not a must. 
  • Modelling of batteries is desired but not a must.

What can you expect of your work situation?

You will work together with a team of enthusiastic scientists with broad experience in automotive systems, battery systems, control systems, and power electronics. Your assignment will be part of a large-scale European project which seeks the further development of batteries. If the time-frame allows, your developments will be validated in real battery system. You will have the opportunity of familiarize yourself with the company’s operation, which opens up the opportunity of building a carrier in TNO beyond your assignment.

What can TNO offer you?

You want to work on the precursor of your career; an internship gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your internship and be given the scope for you to get the best out of yourself. Naturally, we provide suitable internship compensation.

Has this vacancy sparked your interest?

Then please feel free to apply on this vacancy! For further questions don’t hesitate to contact us.

Due to Covid-19 and the consequent uncertainties and restrictions, students who are not residing in the Netherlands may currently not be able to start an internship or graduation project at TNO.

Contact: Róbinson Medina Sánchez
Phone number: +31 (0)6-116 63551

Note that applications via email and third party applications are not taken into consideration.


Apply now



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