Internship | Charge time estimation using continuous learning approach
Education typeuniversity (wo)
TypeInternship and graduation project
Hours a weekFulltime – 40
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 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.
How do you want to contribute to tomorrow's world? How big can your impact be? Come and work at TNO and envision it.
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
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.
Contact: Subhajeet Rath
Phone number: +31 (0)6 114 37550