|Industry:||Software-solutions, Automobile Industry, Automotive Electronics Engineering|
|Classification:||KMU, 70 employees|
|Locations:||Bruchsal, Pulheim, USA (Detroit), China (Beijing)|
|Fokus/ Schwerpunktthemen:||Smart City, Science and Engineering, AI, Data & Analytics, IT, Mobility|
|Start of cooperation:||2019|
|RWTH Innovation services:||R&D-Consulting, Technology-Scouting|
TECHNOLOGY TRENDS ON THE TRIAL
RA-Consulting developes software tools and SW-components for the diagnosis, measurements and calibration of electronic systems in the automotive industry. In addition, the company offers custom software solutions in the industrial sector, especially for databases, embedded systems and telematics applications.
The company is constantly expanding its R&D area in order to be able to meet the current competitive requirements as an innovative company. In doing so, a long-term attention is paid to the future-oriented research portfolio. Within the scope of our cooperation, we enable RA Consulting to conduct targeted technology and IP scouting in order to identify current technology trends in good time and to implement them successfully in the corporate strategy. In addition, we provide intensive support in the initiation of joint research projects with RWTH or other industry partners in the areas of Smart City, Science and Engineering, AI, Data Analytics as well as IT and Mobility.
PROJECTS IN COLLABORATION
(Emerging Projects through the KAM Partnership)
As part of the Smart Load project (BMBF), the RWTH Institute i11 received a research contract from RA Consulting for the development of OTX-based test methods for higher reliability of highly automated electric vehicles.
Project KIsSME (BMWi) with Mindmotiv - OnBoardSystem based on artificial intelligence for intelligent and selective data acquisition of moneuver/scenario data: https://www.rwth-innovation.de/de/aktuelles/alle-neuigkeiten/aktuelle-detailseiten/ra-consulting-und-mindmotiv-b%C3%BCndeln-kr%C3%A4fte
OTHER PROJECTS OF THE COMPANY
The central objective of the project is to transfer the latest findings from research into the formation of pollutants within the engine during transient vehicle operation into practically applicable models for the situation-related selection of the operating strategy in the vehicle, which, in combination with supplementary driver recognition, route prediction and diagnostic functions, ensure the lowest possible emissions in practical vehicle operation. Machine learning (ML) methods are used on several levels. RAC is developing a concept for a HW and SW system for real-time monitoring and feedback of emission-related functions and data for ML-based control and diagnostics. In addition, RAC enables validation of models and strategies.
ASIMOV will increase the autonomy and self-optimization of CPS by creating physically realistic digital twins of these systems and training innovative AI algorithms for CPS control using these digital twins. A wide range of systems will be considered, including electron microscopy, unmanned commercial vehicles, and pulp and paper process control. RAC will develop the concept of a heterogeneous on-board system for data acquisition in the Unmanned Utility Vehicle (UUV) physical system and evaluate it using selected use-case validation tasks.
More Information here.