Internship | Battery Ageing Prediction through Physics-Informed Data-Driven Methods
About this position
The value of an electric vehicle is currently largely determined by the cost of its battery. Hence it is important, especially for fleet operators, to determine the current state of health of the batteries in their vehicles and to understand how long these batteries will last and if any action can be taken to prolong the useful life of the batteries. However, battery ageing is a challenging topic which is as of yet not fully understood by the battery community. Currently, there are several different approaches to modelling and predicting ageing, where the most popular methods are either purely data-driven (machine learning and AI) or physics based.
What will be your role?
You will be predicting battery ageing through a combination of data-driven and physics based methods. Both of these methods have potential, but also come with challenges. To reach their full potential data-driven methods need high quality data in high quantity. However this data is challenging to obtain. Lab data is often of high quality, but low in quantity and expensive to produce. On the other hand, field data is rich in its diversity and quantity, but lacks in quality, as gaps in the data, rogue datapoints and flawed measurements create challenges. Physics-based methods on the other hand face the challenge of requiring very specific data which can only be obtained through cell teardown. The challenge is to combine the best of these two methods.
The goal of this thesis project is to explore how the data-driven methods can be improved through transfer learning based on physics informed features or how the strengths of data-driven and physics-based methods can be leveraged and be combined to overcome their individual shortcoming and together produce more accurate predictions. The most important goal is that the explored approach provides better performance or novel capability compared to existing methods.
Assignment tasks:
- Literature survey on the status of data-driven and physics-based ageing prediction
- Select an approach and design a predictor
- Implement in Python and run on TNO data pipeline
What we expect from you
- Bachelor’s degree in relevant field like Mechanical, Electrical Engineering, Physics, Chemistry or Data Science
- Coursing a master in a relevant field like Data Science/AI or Electrochemistry
- Knowledge of Machine Learning and Python
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:
- A highly professional, innovative internship environment, within a team of top experts.
- A suitable internship allowance (615 euro for wo-, hbo- and mbo-students, for a full-time internship).
- Possibility of eight hours of free leave per internship month (for a full-time internship).
- A free membership of Jong TNO, where you can meet other TNO professionals and join several activities, such as sports activities, (work-related) courses or the yearly ski-trip.
- Use of a laptop.
- An allowance for travel expenses in case you don’t receive an OV-card.
TNO as an employer
At TNO, we innovate for a healthier, safer and more sustainable life. And for a strong economy. Since 1932, we have been making knowledge and technology available for the common good. We find each other in wonder and ingenuity. We are driven to push boundaries. There is all the space and support for your talent and ambition. You work with people who will challenge you: who inspire you and want to learn from you. Our state-of-the-art facilities are there to realize your vision. What you do at TNO matters: impact makes the difference. Because with every innovation you contribute to tomorrow’s world. Read more about TNO as an employer.
At TNO we encourage an inclusive work environment, where you can be yourself. Whatever your story and whatever unique qualities you bring to the table. It is by combining our unique strengths and perspectives that we are able to develop innovations that make a real difference in society. Want to know more? Read what steps we are taking in the area of diversity and inclusion.
The selection process
After the first CV selection, the application process will be conducted by the concerning department. TNO will provide a suitable internship agreement. If you have any questions about this vacancy, you can contact the contact person mentioned below.
Important to be aware of before applying:
- Before the start of the internship, the internship agreement from TNO needs to be signed. For students at a college or university based in the Netherlands, TNO uses the UNL-template (supplemented with a number of specific agreements from TNO). For students of foreign and MBO educational institutions, the TNO internship agreement applies. TNO does not sign any other internship agreements.
- Before the start of the internship, the educational institution will need to confirm in writing that:
- 1) you are enrolled at the educational institution during the internship, and;
- 2) the internship takes place as part of the programme of the study.
- The confirmation of educational institution takes place by signing the UNL template or forms prepared by TNO.
- Interns at TNO must be in possession of a Dutch residential address at the start of the internship. Performance of internship activities from abroad is not possible.
Has this job opening sparked your interest?
Then we’d like to hear from you! Please contact us for more information about the job or the selection process. To apply, please upload your CV and covering letter using the ‘apply now’ button.