AnRox

 

Fail-safe and efficient electric drive system for robot taxis

 

The research focus of the project "Fail-safe and efficient electric drive system for robot taxis (AnRox)" is the development of an optimized drive system for automated electric driving. The methods, simulation tools and solutions developed in the project focus on the use case of a robot taxi. However, they are generally transferable to all electrically driven vehicles. The project is funded by the German Federal Ministry for Economic Affairs and Energy (BMWi).

The objective is to investigate and validate innovative fail-safe drive systems for robot taxis as a main application, in which additional costs are compensated by increased user and operator benefits. This is achieved by a closely interlinked approach across all relevant system levels, starting from the system context (use case & vehicle level) up to the topology (interaction drive & chassis) and component level (on-board power supply, power electronics & machine). Furthermore, degrees of freedom of multi-motor drives and intelligent diagnostic concepts are applied with AI-based methods.

In AnRox, ISEA focuses on concepts for diagnosis and condition monitoring of electrical machines. For this purpose, approaches with and without additional sensors and approaches with signal injection are considered. Furthermore, a metrological evaluation as well as the classification of errors from signal images are carried out. Artificial intelligence, e.g in the form of neural networks, is used for this purpose. In AnRox, simulation models are developed that allow the simulation of faults and, thus, allows a transfer of the concepts to other electrical machines. Furthermore, control strategies reacting to detected faulty behavior are investigated. This includes the evaluation of fault intensity, fault evolution and fault effects. In addition, control strategy for sensor failure is investigated. Here, the idea is to compensate the missing sensor information by using observer models, artificial intelligence, and/or symmetry and steady-state estimations.

 

Duration

01st April, 2021 – 31st March, 2024

 

Funding

   

Partners