Sustainable system design of electric powertrains—comparison of optimization methods. Issue 9 (2nd September 2022)

• Record Type: Journal Article
• Authors: Leise, Philipp; Eßer, Arved; Eichenlaub, Tobias; Schleiffer, Jean-Eric; Altherr, Lena C.; Rinderknecht, Stephan; Pelz, Peter F.
• Is Part Of: Engineering optimization. Volume 54:Issue 9(2022)
• Page Start: 1441
• Page End: 1456
• Publication Date: 2022-09-02
• Subjects: Powertrain -- stochastic optimization -- global optimization -- genetic algorithm; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2021.1928660
• Languages: English
• ISSNs: 0305-215X

Abstract

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.