AI4CSM

Automotive Intelligence 4 Connected Shared Mobility

The AI4CSM project (Automotive Intelligence for Connected Shared Mobility) is a European research initiative with 41 partners from 10 countries, funded through the Horizon 2020 (H2020) program. Its goal is to apply artificial intelligence (AI) to develop sustainable mobility solutions. This includes optimizing charging infrastructure for electric vehicles, improving vehicle-to-cloud connectivity with over-the-air updates and enhanced cybersecurity, and promoting sharing economy models for more efficient resource utilization. Additionally, advanced driver assistance systems are being developed to enable a gradual transition to autonomous driving. The Institute of Lightweight Engineering and Polymer Technology (ILK) at TU Dresden has been researching an innovative method for foreign object detection in inductive charging systems. Foreign objects in the air gap between the transmitter and receiver coils can heat up significantly due to the strong magnetic alternating field, potentially causing damage or burns. The new method utilizes electrical time-domain reflectometry to measure impedance changes in a transmission line caused by nearby foreign objects. This enables comprehensive detection of both metallic and non-metallic objects without blind spots. Additionally, artificial neural networks are used to interpret the sensor signals, enhancing the robustness of detection and enabling localization and identification of foreign objects. Compared to conventional methods, this approach allows for a simplified sensor design with greater precision and reliability.

3D representation: rectangular object with wavy lines on the surface, similar to a heating pattern. Blue arrows point upwards, metallic circle in the front center, above which red arrows point upwards. Background: diagram representation. — Visualisation of the sensor principle: The local wave impedance of the sensor's transmission line changes with the proximity of objects. Using electrical time domain reflectometry, the impedance changes can be detected.

Photo of a plate on a table, wrapped in glass or plastic. Thin wavy lines on the surface of the rectangle. — Automated test and validation of the Demonstrator on a XYZ test bench.

Period

01.05.2021 – 31.03.2025

Project partners

Funding

Federal Ministry of Education and Research (BMBF)
Fundingcode: 101007326

Logos of the European Union and the Joint Undertaking (JU) ‘Electronic Component Systems for European Leadership’ (ECSEL)

This research project is part of the AI4CSM project that has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No. 101007326. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Belgium, Czech Republic, Italy, Netherlands, Lithuania, Latvia, France, Sweden, Norway. The project was also funded by the German Federal Ministry of Education and Research under grant number 16MEE0173.