Credibility-based optimisation of (hybrid-) electric aircraft

Abstract

Designs concerning (hybrid-) electric aircraft often have large discrepancies in the underlying assumptions regarding highly influential design parameters, mostly regarding the predicted performance of batteries, fuel cells, electric motors and novel airframe technologies. These variations make it difficult to assess the viability of a proposed novel aircraft design.
The concept of design credibility is introduced, to add a layer of chance-of-realisation to aircraft design and respective performance predictions. For parameters with a high influence on the viability and performance of electric aircraft, probability curves are created that can predict a range of assumed future performance by 2035. With these curves, the credibility of each parameter as well as total aircraft performance are calculated.
A new energy network model is presented that is able to model the main components of hybrid-electric networks individually. Network sensitivity studies show that uncertainties in network voltages, conductor materials and electric machine performance can have a relevant impact on the aircraft size. The addition of thermal management systems show mass increases sensitive to the system architectures.
A credibility-based multidisciplinary design optimisation framework using the aircraft mission simulation tool SUAVE and the novel energy network is created. In four case studies, two fully-electric CS-23 and two boosted-turbofan CS-25 designs are optimised for maximum achievable mission range under credibility constraints.
Optimisation results show that battery gravimetric energy density is a dominating factor. The influence of motor and airframe parameters on the optimal design depends on the desired credibility level. For high credibilities, the motor is more relevant. For low credibilities, airframe technology improvements are increasingly dominant. The shape of the underlying credibility distributions of secondary parameters have a strong effect on their relevance for the optimal configuration.
Credibility-based optimisation can be used in the design optimisation phase and as a second step on a finalised design.

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