About this position
Battery packs are currently driving the energy transition in the mobility sector. A battery pack consist of many individual battery cells connected in series or parallel. To ensure proper functioning and safety of all the individual cells, packs are monitored by a Battery Management System (BMS). A BMS is an embedded system which measures voltage, current and temperature to estimate battery metrics such as the state-of-charge. Such estimations are based on ‘empirical’ models (such as equivalent circuit models) that represent the battery. These models face limitations while predicting internal battery states, critical for predicting battery degradation (i.e., ageing) and extending its useful lifetime. In this assignment, you will be developing BMS algorithms based on a modelling approach that uses physics to capture the internal battery states to increase the useful lifetime of a battery.
What will be your role?
Currently, Battery Management Systems typically use empirical models, which focus on predicting output (voltage) based on input (current). While these models can effectively be used for voltage prediction, they are not suitable for monitoring or enforcing physical limits inside the cell that can cause, for example, cell ageing. For this reason, Physics-Based Models (PBMs) are receiving significant attention in research and industry because they can simulate internal states, including ageing reactions. For example, these models are being used for developing fast charging algorithms that prevent ageing in the battery. While scientific literature provides several examples of using these models, their applicability to real-life scenarios still remains uncertain due to lacking of parameters, computational complexity, etc.
The goal of this thesis project is to explore the opportunities of using PBM for practical applications on Battery Management Systems. Possible applications can range from, for example, developing state observers for concentration monitoring in PBM, PBM parametrization, ageing prediction or dynamic charging using model predictive control. The most important goal is that the explored algorithm provides better performance or novel capability compared to existing empirical methods, and that it is useful in real-life scenarios. This assignment is initially intended as an master thesis, but can also be treated as a master thesis + internship.
Assignment tasks:
What we expect from you
What you'll get in return
You want an internship opportunity 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 work placement and be given the scope for you to get the best out of yourself. Furthermore, we provide: