Regional Eco Mobility 2030 (REM 2030)

Short description of the project

The REM 2030 project is a building block for the development of tomorrow's mobility and is under the guiding theme of efficient regional individual mobility 2030. An interdisciplinary team from Baden-Württemberg is developing and evaluating holistic concepts for efficient regional individual mobility. The consideration of a systemic approach, which combines the topics vehicle, infrastructure and new business models, is central to this.

Project goals

Baden-Württemberg universities, Fraunhofer institutes and industry work closely together in the innovation cluster. The concepts include:

1. the locally emission-free operation of passenger cars in cities and conurbations
2. an innovative and efficient drive system technology for electric mobility
3. a lightweight design optimized for electromobility
4. Driver assistance systems and mobility assistants
5. the user-friendly operation with multifunctionality
6. the energy-efficient use as well as the energy-economic integration.
   

The solutions developed are integrated into vehicle platforms for testing and external presentation as well as for acceptance analyses. The project draws on developments in other mobility projects.

Project result

In the Department of Measurement, Control and Diagnostic Systems (MRD), a mobility assistant for increasing fuel efficiency is being developed as part of the REM 2030 project. This driver assistance system supports the driver while driving to adopt a fuel-saving and environmentally friendly driving style. Both internal and external vehicle characteristics and influences are taken into account. The driver must not be overtaxed or distracted by the system during the entire journey. In concrete terms, the work can be divided into the following steps:

• Development of a vehicle model that takes internal and external physical influences into account.
• Integration of infrastructure information from the environment such as traffic signs, signal systems and road topology.
• Inclusion of other road users.
• Estimation of current driver preferences.
• Model predictive optimization of current and future driving behavior based on existing information and estimates.
• Generation of driver-friendly driving instructions and instructions.
• Development of an intuitive HMI for communication between system and driver.
• Validation of the system using simulations and real experiments.