Spring 2026 Master Thesis Data driven modeling of battery energy storage system efficiency

What We Do

Swiftly gaining ground as Swedens industry leader in battery storage and expanding rapidly in selected European markets Flower is on a mission to enable the energy system of tomorrow.

With an industry-leading AI-powered platform at its core our service includes stabilizing the energy system by enhancing predictability and flexibility for both energy producers and consumers. By combining pioneering technology with a portfolio of flexible energy assets we break new ground towards a fossil-free energy system allowing clean energy to power society.

Who We Are

Flower is Flexible Power. We are a next-gen energy company leveraging AI and machine learning to make renewable energy stable and always available even when the sun isnt shining and the wind isnt blowing.

Through smart optimization and trading of energy assets like wind and solar farms battery systems and EV chargers we make renewable energy reliable and predictable leading the charge towards the energy system of tomorrow.

About the Master Thesis subject:

Gridscale BESS are increasingly important for balancing the power system but in practice their performance depends on many interacting factors: power level temperature system efficiency auxiliary loads (HVAC control systems etc.) and battery state of health (SoH).

Being able to predict how energy availability is changing due to these parameters is crucial when using batteries to trade on power and energy markets in order to avoid costly fees and deliver accurately on our this thesis you will use real telemetry from Flowers BESS to figure out how we can improve the trading and optimization of grid-scale battery assets.

What Youll Do:

The goal is to build a datadriven understanding and preferably a simple model of how BESS resources behave under real operation.

Example tasks:

• Perform data analysis to characterize auxiliary loads for assets in our portfolio.
• Make use of the existing data set to improve the teams understanding of optimal BESS operation.
• Separate true efficiency losses from auxiliary consumption as far as the data allows.
• Propose and fit a model that predicts auxiliary loads and efficiency losses.
  
  Part of your task will be to help define innovative ways of approaching this problem with support from our team
  
  Your background:
• Background in energy systems control electrical engineering applied mathematics physics or similar.
• Solid background in data analysis and interest in battery system modeling.
• Strong analytical skills and an interest in data visualization.
• Curious about battery technology and its role in the power system.
  

The project can be carried out by one or two students in collaboration.

Location
Our beautiful office is located in the heart of Södermalm just a short walk from Slussen subway station. We prefer in-office collaboration for this matter.

Apply
To apply please submit a CV and answer the questions thoroughly at the beginning of the application.

We look forward to hearing from you!

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