TY  - JOUR
A1  - Leise, Philipp
A1  - Eßer, Arved
A1  - Eichenlaub, Tobias
A1  - Schleiffer, Jean-Eric
A1  - Altherr, Lena
A1  - Rinderknecht, Stephan
A1  - Pelz, Peter F.
T1  - Sustainable system design of electric powertrains - comparison of optimization methods
JF  - Engineering Optimization
N2  - The transition within transportation towards battery electric vehicles can lead to a more sustainable future. To account for the development goal ‘climate action’ stated by the United Nations, it is mandatory, within the conceptual design phase, to derive energy-efficient system designs. One barrier is the uncertainty of the driving behaviour within the usage phase. This uncertainty is often addressed by using a stochastic synthesis process to derive representative driving cycles and by using cycle-based optimization. To deal with this uncertainty, a new approach based on a stochastic optimization program is presented. This leads to an optimization model that is solved with an exact solver. It is compared to a system design approach based on driving cycles and a genetic algorithm solver. Both approaches are applied to find efficient electric powertrains with fixed-speed and multi-speed transmissions. Hence, the similarities, differences and respective advantages of each optimization procedure are discussed.
KW  - Powertrain
KW  - stochastic optimization
KW  - global optimization
KW  - genetic algorithm
Y1  - 2021
U6  - http://dx.doi.org/10.1080/0305215X.2021.1928660
SN  - 0305-215X
PB  - Taylor & Francis
CY  - London
ER  -