Federated Learning & Edge Processing for Safe and Efficient Operation

Background

The methods for data collection and processing in the mobility sector have remained relatively unchanged over the past two decades. However, emerging challenges – best exemplified by electromobility – necessitate a novel approach to building these systems, with enhanced flexibility in storage and data management. Edge processing offers the potential for a new data logging infrastructure by reducing transmission costs and lowering analytics latency, benefitting vehicle manufacturers, fleet owners and drivers.

Goal and scope

The FREEPORT project aims to support electromobility transformation by addressing three key challenges faced by heavy-duty vehicle operators today: efficiency, safety and uptime.

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.