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.
RESULTS SO FAR
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.