Battery modelling means creating some sort of a mathematical representation of how a battery behaves. Such models help us understand batteries, control them, and even predict their future performance. In almost every battery system, there is some kind of model behind the scenes, for instance to estimate the remaining range of an electric vehicle or to show the user of a smartphone the state of charge. Models are an essential part of how we monitor and operate batteries.

Most state of the art models rely on ideal assumptions and are calibrated for controlled laboratory conditions. This well in test setups, but once the battery is used in the field, the situation changes. Temperatures vary, load profiles are irregular, and measurements can be noisy or incomplete. These conditions quickly reveal the limits of standard models. They may give inaccurate predictions or fail to capture important effects like degradation under real usage.

In my PhD research, I focus on improving this situation. I develop models that stay accurate and stable even when applied to real-world data. My work combines classical electrical equivalent circuit models with data-driven methods. The goal is to combine the physical understanding of the battery with the flexibility of data-based approaches, so the models can handle noisy signals and still deliver reliable estimates of states such as the state of charge and state of health.

During my secondment at the University of Bristol, I joined the group of Prof. Alastair Hales, whose research is closely related to my own and could perfectly complement my work. Being part of their team and sharing an office with colleagues working on similar topics created an ideal environment for exchange and collaboration. I had many opportunities to discuss different modelling approaches and learn from the group’s experience, particularly in areas such as modelling the hysteresis behaviour of batteries and thermal modelling. These discussions gave me new perspectives on how to extend and validate my models under real-world conditions. The stay also helped me build valuable connections within the research community and broaden my understanding of how different methods can be combined to address practical challenges in battery modelling.

Outside the lab, Bristol is a beautiful and lively place. The city has a special charm, and on the image on top of this post you can see the famous Clifton Suspension Bridge, one of my favourite spots. After work, I often went running or cycling through the surrounding nature, which was a great way to clear my head and think about new ideas. This mix of inspiring research and time outdoors made the stay a really valuable experience, both scientifically and personally.

I would like to thank the whole team at the University of Bristol for their warm welcome and collaboration. A special thanks goes to Alastair, Mark, Aya, Gaurav, Andreas, and Renze, who made this secondment especially meaningful and rewarding.

Paul Busch

RWTH Aachen – University of Bristol