AbstractThis thesis details research undertaken to develop novel techniques to deliver an automated methodology for optimising hybrid electric powertrains. Uniquely the target for optimisation is high performance road and track cars. Novelty in the research described includes the use of existing components; evaluating multiple topologies, and optimising for multiple, often conflicting, objectives.
The problem statement is based on the need to optimise complex powertrain systems in a more virtual development cycle to reduce the use of physical prototype vehicles and deliver more optimised powertrains in a shorter timeframe.
A genetic algorithm (GA) approach was used as a baseline process. The thesis then describes a series of improvements and best practices for implementing this GA approach in the real world. The research concluded that the optimisation algorithms can be better used to solve hybrid electric vehicle development as an automated search tool, rather than an optimisation process. Based on this, multiple techniques were implemented to reduce the search space and improve the efficiency of this search, it resulted in 62% in time savings and over 39% less evaluations.
Among these changes a graphic user interface to define components and topologies of interest, was developed, prioritising objectives, adding restrictive penalties (to reduce wasted simulation effort), and building a test database that can be used by the search algorithm to avoid evaluating repeated topologies. Additionally, this database serves as the outcome for the user to improve the confidence in his selection, gain insights on vehicle sensitivities, and as an historic database that can be further grown with new studies.
|Date of Award
|23 Mar 2022
|McLaren Automotive Limited, Institute of Digital Engineering & Ricardo UK Limited
|Sam Akehurst (Supervisor), Chris Brace (Supervisor) & James Turner (Supervisor)
- hybrid electric vehicle (HEV)
- optimisation technique
- Genetic Algorithms