Predictive Energymanagement for BEV

Coordinator Bernhard Rolle, M. Sc.
Partner Daimler AG

 

 

 

Description

Mandatory CO2 standards for new cars and vans, to be implimented as from 2020, were introduced by the European Parliament in 2013. A target value of 95g CO2/km was set. In view of ambitious regulations worldwide, high expectations are placed on electric mobility. Electric vehicles (EV) and in particular battery electric vehicles (BEV) are seen as a key technology with regard to sustainability and economical growth. Even with further development of the battery capacity, the overall range for BEVs still remains a challenge.

It is mainly expected that further research on energy storage systems will enhance the range of BEVs. Additionally an improvement can be achieved by sophisitcated controls (supervisory and low-level algorithms) for battery and thermo managment as well as propulsion systems. Aim of this research project is to exploit the increasing amount of available traffic and navigation data to improve the energy management of BEV. Contrary to the projects Predictive eco driving strategy for EV and HEV and Thermal Management for EV emphasis is put on propulsion control strategies and electric drives. 

BordnetzkonfigurationThe investigation is focused on predictive and loss minimizing control schemes for induction and synchronous machines with reversible field intensity. A model predictive control approach allows to utilize road preview data and consider constraints which arise from operational limitations posed by the battery and thermo management as well as the physical nature of the power train. In the course of the development of BEV with all-wheel drive dual-motor configurations the predictive enery management has to include an intelligent power splitting strategy for different driving modes. 

Funding

Sponsored by the state Baden-Württemberg in cooperation with domestic universities and industry.Support program: Promotionskolleg Hybrid