Work Package 3: Extended Driving Range

(Updated: December 2018)


Extend the driving range of electric vehicles by securely exploiting the operating conditions of a given battery technology combined with advanced energy management methodologies.

PARTNERS: RWTH(lead), Siemens, Algolion, TU/e, Voltia, VDL-ETS


  • Combine both a wider voltage range during driving operation, allowing a larger SoC range to be used, as well as higher charging powers during grid-charging.
  • Develop techniques to determine vehicle power requests (depending on vehicle state, road and weather conditions) such that battery state can be translated into accurate vehicle driving range estimation.
  • Develop vehicle energy management and driver coaching methodologies to extend driving range by more efficient energy usage.



  • High resolution driving data was recorded by VOLTIA and is now used for algorithm development and realistic cell ageing.
  • At RWTH Aachen, an embedded system using a PI controller to determine the optimal charge current for fast charging was described and tested.
  • ALGOLiON developed, implemented and tested their main algorithm for cell data analysis, databases and some supplemental tools.
  • TUE determined several vehicle road load parameters of a VDL-ETS bus using vehicle tests (dynamometer tests and coast-down tests) and established significant road surface and temperature dependency of the rolling resistance coefficient.
  • VDL ETS developed and validated simulation models of all main components in the bus together with Siemens PLM.


  • Further development and validation of model-based algorithms for faster charging and dynamic battery operating window (RWTH)
  • Development of the Algolion Algorithms for integration in real battery pack
  • Determination of vehicle road load parameters of a Voltia Van (TUE)
  • Development of a driver model (TUE & VDL)
  • Integration of developed algorithms into the two demonstrators
  • ALGOLiON’s software will be embedded into the project’s BMS.