astrid mathilde

When EV charging slows down, the user is often left guessing. Is the charger underperforming? Is it the car? Is something wrong? Or is this simply expected charging behavior?

Public EV charging is much more complex than the interaction often suggests. It is not like filling fuel from one tank to another. Charging speed is shaped by the car, the battery, the connector, available power, site load, and the ongoing communication between the vehicle, charger and on-site system

In collaboration with Siemens, we have explored how the invisible charging process can become understandable. We have designed a scalable feedback language for EV charging, starting with the charge point display, but made to extend across mobile follow-up and future touch points.

Instead of treating the screen a flat surface for information, we have explored how we can use light, rhythm and density as design materials. This became a way to communicate what the system is doing, the current state, and how charging intensity changes over time.

Our visual reference came from electrons, not as a literal scientific simulation, but as a material metaphor for charging behavior. In the starting and preparing phases, the electron cloud represents an active but unresolved state: The car and the charger are communicating, checking and negotiating before energy transfer begins. Once charging starts, this cloud becomes a more directed stream, where density, brightness and rhythm communicate charging power.

The motion is not decoration added on top of the interface. It is a part of the feedback itself, allowing users to sense state and intensity before they have to interpret the numbers. And it makes an invisible technical process feel present, legible and trustworthy.

Alongside this motion language, we have designed the interaction structure around clear states, short explanations, visual hierarchy, timing and transitions. The display guides the user from connection and payment, to charging status, speed and follow-up. Always prioritizing what the user needs to understand in the moment.

We have developed the project through field observations, user interviews, market analysis, system mapping, prototyping and testing. Across this work, the key insight was consistent: Users didn't need more raw data. They needed the system to explain itself in a language most users can understand.

For Siemens, the project proposes more than a redesigned charging screen: It proposes the beginning of a standardized interface for public EV charging, one that can work across different operators, locations and charging situations. In a fragmented charging market where all the actors have different systems and interfaces, this project shows how aesthetic quality can become a part of usability.

The text above is written with assistance from generative AI, and the image below is manipulated using GPT Image 2.

AI usage

We have collaborated with AI across the entire design process. We have not used AI for making design sketches or design prototypes, but for structuring and understanding user insights, and for making working prototypes to be used during user testing. We have also used generative AI during motion exploration.

What I have learned

During this project I have learned the value of using generative AI to make working prototypes based on design prototypes. The weekly presentations throughout the project has helped me practice communicating the design process and articulate design decisions. And I have once again experienced how valuable it is to work in pairs as designers when each other’s strengths and weaknesses complement each other.

This project won us the Prize for Material Exploration and Aesthetic Experience at the AHO WORKS AWARDS.