Internship | Physics-Based Battery Management Algorithm Development – TNO – Helmond

  • Helmond

TNO

About this position

Battery packs consist of many individual battery cells connected in series or parallel. To ensure proper functioning of all the individual cells, they are monitored by a Battery Management System. This consists of a read-out circuit, which does basic measurements like voltage, current and temperature, and a controller, which computes operational parameters such as the state-of-charge of the batteries. At the heart of the controller, a model of the batteries is used to estimate internal states based on measurable external parameters such as voltage and current or to make predictions about it behaviour in the near future.

What will be your role?

Currently, the applied model is typically empirical, meaning that it focusses on predicting output (voltage) based on input (current). While this model can effectively be used for some tasks, it is not suitable for monitoring or enforcing physical limits inside the cell. For this reason, Physics-Based Models (PBMs), which can simulate internal potentials and concentrations inside the cell including ageing reactions, have received significant attention in research and industry. Concrete applications of these models can be to govern fast charging by monitoring the anode potential or to provide power limits which prevent ageing. While literature provides several examples of PBMs running on BMS chipsets, the extent to which these models can be exploited for improving battery management is little concrete.

The goal of this thesis project is to explore the opportunities that a PBM running on a BMS chipset can enable and how the algorithms that use this model can be shaped. Possible applications can range from, for example, concentration monitoring by pairing a PBM with a state observers or dynamic power limits through the use of model predictive control. The most important goal is that the explored algorithm provides better performance or novel capability compared to existing empirical methods.

Assignment tasks:

  • Literature survey on the status of PBM in BMS and the possible uses;
  • Select a use and design controller or observer structure;
  • Implement in Simulink and generate C-code;
  • (optional) Run on embedded platform.
  • What we expect from you

  • Bachelor’s degree in relevant field like Mechanical, Automotive or Electrical Engineering;
  • Coursing a master in a relevant field like Mechanical, Electrical or Control Engineering;
  • Knowledge in Matlab/Simulink and Python.
  • What you’ll get in return

    You want to work on the precursor of your career; a work placement 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 work placement and be given the scope for you to get the best out of yourself. Naturally, we provide suitable work placement compensation.

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