TY  - JOUR
A1  - Meyer, Max-Arno
A1  - Granrath, Christian
A1  - Feyerl, Günter
A1  - Richenhagen, Johannes
A1  - Kaths, Jakob
A1  - Andert, Jakob
T1  - Closed-loop platoon simulation with cooperative intelligent transportation systems based on vehicle-to-X communication
JF  - Simulation Modelling Practice and Theory
Y1  - 2021
U6  - http://dx.doi.org/10.1016/j.simpat.2020.102173
SN  - 1569-190X
VL  - 106
IS  - Art. 102173
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Schulze, Sven
A1  - Feyerl, Günter
A1  - Pischinger, Stefan
T1  - Advanced ECMS for hybrid electric heavy-duty trucks with predictive battery discharge and adaptive operating strategy under real driving conditions
JF  - Energies
N2  - To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks.
KW  - Energy management strategies
KW  - ECMS
KW  - CO2 emission reduction targets
KW  - Driving cycle recognition
KW  - Predictive battery discharge
Y1  - 2023
U6  - http://dx.doi.org/10.3390/en16135171
SN  - 1996-1073
N1  - The article belongs to the Special Issue "Energy Management Strategies of Electrified Vehicles toward the Real-World Driving".
VL  - 16
IS  - 13
PB  - MDPI
CY  - Basel
ER  -