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Internship | Non-Intrusive Model Identification of a Physics-Based Battery Model

Graduate Internship Helmond, NL
Posted 30 Oct 2020

Degree level


Job function


Required languages

Dutch (Fluent)
English (Fluent)

Work experience

0 to 1 years

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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. 


  • 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.


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.


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.


Innovation with purpose: that is what TNO stands for. We develop knowledge not for its own sake, but for practical application.
TNO connects people and knowledge to create innovations that boost the competitive strength of industry and the well-being of society in a sustainable way.
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Den Haag
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