Internship | Charge time estimation using continuous learning approach
About this position
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 charging infrastructure. Such charging infrastructure, is mostly given by chargers. A charger is a device that provides electrical energy from the grid (i.e., AC) to the vehicle (i.e., AC or DC). Large scale deployment of chargers is limited not only by economic constraints but also by limitations in the grid capacity. One strategy to deal with the grid-capacity limitation consists of dynamically allocating the time slots when chargers are connected to vehicles. This is commonly known as charger scheduling. Charger scheduling requires (among other things) knowledge of the charging time of each vehicle. The total charging time of a vehicle will then determine how long a vehicle will be connected to a particular charger. A precise estimation of the charging time is therefore crucial for an efficient assignment of chargers to vehicles. The charging time of a vehicle is influenced by many factors such as different energy requirements, battery capacity, input power, temperature, etc. Further, batteries degrade over the years (i.e., age) yielding to undesired side factors such as reduced capacity or increased resistance. Therefore, battery ageing also has an impact on charging time.
What will be your role?
The purpose of this assignment is to use machine learning approaches to create a model that can predict the charging time of a vehicle. The approach must take into consideration all the relevant factors that influence the charging time (e.g., required energy, battery capacity, etc). Further, the approach must also be able to continuously learn with the latest charging data, such that the charging time is corrected as the battery ages.
The assignment covers the following tasks:
- Dataset generation with different battery models/ages/charging strategies.
- Continuous-learning algorithm implementation with and without battery ageing correction.
- Algorithm testing, quality of prediction comparison with and without ageing correction.
- Reporting writing.
- Final presentation to colleagues.
What we expect from you
- You have a bachelor’s degree in a relevant field, for example automotive engineering, data science, electrical engineering.
- You are coursing a master in a relevant field, for example automotive engineering, data science, electrical engineering
- Knowledge in Matlab/Simulink
- You enjoy developing innovative solutions to technically challenging problems and have the right mix of intellectual curiosity and pragmatism.
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.
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.
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.
More information about this vacancy?
Anne-Maartje den Uijl-MeijmanFunctie:Recruiter