FREEPORT: Federated Learning and Edge Processing for Safe and Efficient Operation

Purpose and goal

The FREEPORT project aims to support electromobility transformation by addressing three key challenges faced by heavy-duty vehicle operators today: efficiency, safety, and uptime. This goal can be facilitated by performing computations close to the source of the data instead of a central location. This project leverages edge computing to reduce transmission costs and lower analytics latency, benefiting vehicle manufacturers, fleet owners, and drivers.

Expected effects and result

The business value and use cases encompass monitoring electric components such as batteries and motors, developing foundations for using third-party services in edge devices, energy consumption predictions to optimise charging, and improving functional safety through continuous surveillance to alert the operators as needed. We expect to demonstrate edge data collection and processing for at least 20 vehicles, with the goal of connecting 50 heavy-duty electric trucks by the project´s conclusion.

Planned approach and implementation

FREEPORT will develop cutting-edge data analytics capabilities on edge: novel real-time streaming anomaly detection algorithms tailored to the automotive sector, a versatile event-based data collection framework, a cybersecurity-aware architecture for real-time safety alerts, and comparative evaluation of state-of-the-art federated learning methods. The potential of edge processing and learning will be showcased using AI Sweden Edge Learning Lab to a broader audience.