TY  - CHAP
A1  - Leise, Philipp
A1  - Altherr, Lena
A1  - Simon, Nicolai
A1  - Pelz, Peter F.
T1  - Finding global-optimal gearbox designs for battery electric vehicles
T2  - Optimization of complex systems - theory, models, algorithms and applications : WCGO 2019
N2  - In order to maximize the possible travel distance of battery electric vehicles with one battery charge, it is mandatory to adjust all components of the powertrain carefully to each other. While current vehicle designs mostly simplify the powertrain rigorously and use an electric motor in combination with a gearbox with only one fixed transmission ratio, the use of multi-gear systems has great potential. First, a multi-speed system is able to improve the overall energy efficiency. Secondly, it is able to reduce the maximum momentum and therefore to reduce the maximum current provided by the traction battery, which results in a longer battery lifetime. In this paper, we present a systematic way to generate multi-gear gearbox designs that—combined with a certain electric motor—lead to the most efficient fulfillment of predefined load scenarios and are at the same time robust to uncertainties in the load. Therefore, we model the electric motor and the gearbox within a Mixed-Integer Nonlinear Program, and optimize the efficiency of the mechanical parts of the powertrain. By combining this mathematical optimization program with an unsupervised machine learning algorithm, we are able to derive global-optimal gearbox designs for practically relevant momentum and speed requirements.
KW  - Powertrain
KW  - Gearbox
KW  - Optimization
KW  - BEV
KW  - WLTP
Y1  - 2019
SN  - 978-3-030-21802-7
U6  - http://dx.doi.org/10.1007/978-3-030-21803-4_91
SP  - 916
EP  - 925
PB  - Springer
CY  - Cham
ER  - 
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  -