Reducing EV range anxiety: How a simple AI model predicts port availability

The transition to electric vehicles (EVs) is accelerating globally, bringing with it the critical need for a reliable and robust charging infrastructure. While building out more physical charging stations is an important step, an equally important task is maximizing the efficiency of this infrastructure and minimizing "range anxiety”, a term used to describe an EV driver’s fear of running out of battery before reaching their destination or the nearest available charging station. These concerns led us to design an approach for EV routing that reduces range anxiety by integrating charging stations into the navigational route based on the battery level and destination.

This week we announced a new lightweight, highly efficient prediction model that can answer the core question, “What is the probability that an EV charging port will be available at a specific station a certain number of minutes from now?” We found that the most sophisticated model isn't always the best solution. By co-designing the model and the deployment infrastructure, we were able to create a highly effective prediction system based on a simple linear regression approach. This model’s simplicity is its strength, allowing it to rely on easily accessible features while still achieving performance improvements over a strong baseline. Our work demonstrates that combining intuitive real-world logic with machine learning can deliver significant operational and user experience benefits.

Creating the model

Our goal was to maximize predictive power while minimizing the feature set (i.e., the specific, measurable data points the model uses to make a prediction) to ensure speed and low-latency deployment. After testing various architectures, including a decision tree and a simple neural network, a straightforward linear regression model proved to be the most performant and robust for this specific task.