The success of battery electric trucks depends largely on efficient and cost-effective charging. Such a charging can be achieved at logistic terminals when loading, unloading or when the trucks are parked overnight. However, when the fleets of battery-electric trucks grow, the charging at logistic terminals becomes increasingly more challenging. How do you ensure that the trucks can carry out their transport tasks within the limits of the electric grid and the available chargers? As the terminal’s grid connection often is rather weak, there is a technical challenge to ensure that the peak power is kept as low as possible. From a commercial perspective, the accumulated charging profile from a single site impact both the power and energy costs. They can be minimized when actively controlling when and at what power level individual trucks are charged. The complexity in the control of the chargers increase further as many terminals also invest in photovoltaics and stationary batteries.

The aim of the master thesis is to create an optimization algorithm(s) for fleet charging at a terminal, considering the aspects mentioned above. In addition, evaluate the developed algorithm(s) by comparing with simpler charging strategies and assess how such an algorithm could be implemented in the existing transport system. Depending on the student’s preference and academic supervisors’ recommendations the work can be expended in different directions, such as energy system integration incl. PV & battery, explore system integration into route planning, physical layout and transport flows within the terminal, the role of machine learning etc.

The master thesis work will be part of innovation project “Local Energy systems for Electrified Logistic Applications” (LEELA). Modeling will be either based on data from the industry or using artificial data mirroring realistic conditions. The work will include result sharing of main milestones and final results with the project consortium for feedback gathering and knowledge dissemination to the industry.

Students background:
Relevant Master’s degree programme

Skills:
Python or equivalent

Location:
Preferably at Lindholmen Science Park, Folkungagatan 44 Stockholm, but there is also option for remote work

About Lindholmen Science Park and Closer
Lindholmen Science Park AB works without profit towards the vision of strengthening Sweden's competitiveness with a focus on tomorrow's mobility for people and goods. Our national mission is to run programs and activities for innovation and research. One of Lindholmen Science Parks programs is CLOSER, a neutral collaboration platform, knowledge node and project workshop for increased transport efficiency and well-functioning logistics. LEELA project runs within the CLOSER program.
We offer a neutral development environment where actors from business, academia and society can run large research and development projects together. For 20 years, we have been involved in creating successful collaborations and new knowledge. Creating the space for new networks, new skills and new opportunities for development and growth.

Contact person and supervisor at company
Janis Danebergs (MSc in Energy Systems)
Janis.danebergs@lindholmen.se
072 150 55 41

Application process
Write a short motivation letter to Janis. As the master thesis for the spring will start soon the selection process will be dynamic, but most probably will include at least one interview for relevant candidates.