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Sustainable system design of electric powertrains—comparison of optimization methods.

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Titel: Sustainable system design of electric powertrains—comparison of optimization methods.
Autoren: Leise, Philipp, Eßer, Arved, Eichenlaub, Tobias, Schleiffer, Jean-Eric, Altherr, Lena C., Rinderknecht, Stephan, Pelz, Peter F.
Quelle: Engineering Optimization; Sep2022, Vol. 54 Issue 9, p1441-1456, 16p
Schlagwörter: SYSTEMS design, SUSTAINABLE design, ELECTRIC vehicle batteries, GENETIC algorithms, CLIMATE change mitigation, AUTOMOBILE power trains
Firma/Körperschaft: UNITED Nations
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:0305215X
DOI:10.1080/0305215X.2021.1928660