Overview

Use Case I – Data-driven EV Optimisation

Credits: Figure uses graphical data of the project partner Elaphe

Use Case I has two targets: from the side of EV engineering, the focus is on new methods of the powertrain control with enabled optimisation using AI components based on the analysis of the actual on-board data and collected historic data about the EV operation; from the side of the SDV architecture, the goal is to demonstrate the implementation of the vehicle-to-cloud (V2C) services for improving EV efficiency. The project will use an original V2C service that will be adopted to the CODE4EV SDV architecture.

Use Case II – EV Health Monitoring and Predictive Maintenance

The second Use Case has focus on chassis and powertrain health monitoring with mixed on-board and cloud-based diagnosis. The component level introduces a set of sensor fusions for the vehicle systems. Both common vehicle sensors and additional soft sensors are being used for the monitoring. The strategy of the health monitoring in CODE4EV is targeting continuous analysis of operational states of brake, chassis, battery, and powertrain systems as especially relevant for EV.

Use Case III – Smart Motion Control

This Use Case deals with safety-critical functions and should demonstrate benefits of SDV concepts and AI-based control approaches for vehicle dynamics control realised by systems as ESP, torque vectoring et al. At that the vehicle should not only demonstrate required driving safety but also increase of energy efficiency through integrated operation of powertrain and chassis systems with smart redistribution of control efforts.