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Cascade Optimisation of Battery Electric Vehicle Powertrains

Matheus Torquato Orcid Logo, Kayal Lakshmanan, Natalia Narożańska, Ryan Potter, Alex Williams Orcid Logo, Fawzi Belblidia Orcid Logo, Ashraf Fahmy Abdo, Johann Sienz Orcid Logo

Procedia Computer Science, Volume: 192, Pages: 592 - 601

Swansea University Authors: Matheus Torquato Orcid Logo, Kayal Lakshmanan, Alex Williams Orcid Logo, Fawzi Belblidia Orcid Logo, Ashraf Fahmy Abdo, Johann Sienz Orcid Logo

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Abstract

Motivated by challenges in the motor manufacturing industry, a solution to reduce computation time and improve minimisation performance in the context of optimisation of battery electric vehicle powertrain is presented. We propose a cascade optimisation method that takes advantage of two different v...

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Published in: Procedia Computer Science
ISSN: 1877-0509
Published: Elsevier BV 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa58380
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Abstract: Motivated by challenges in the motor manufacturing industry, a solution to reduce computation time and improve minimisation performance in the context of optimisation of battery electric vehicle powertrain is presented. We propose a cascade optimisation method that takes advantage of two different vehicle models: the proprietary YASA MATLAB® vehicle model and a Python machine learning-based vehicle model derived from the proprietary model. Gearbox type, powertrain configuration and motor parameters are included as input variables to the objective function explored in this work while constraints related to acceleration time and top speed must be met. The combination of these two models in a constrained optimisation genetic algorithm managed to both reduce the amount of computation time required and achieve more optimal target values relating to minimising vehicle total cost than either the proprietary or machine learning model alone. The coarse-to-fine approach utilised in the cascade optimisation was proven to be mainly responsible for the improved optimisation result. By using the final population of the machine learning vehicle model optimisation as the initial population of the following simulation-based minimisation, the initial time-consuming search to produce a population satisfying all domain constraints was practically eliminated. The obtained results showed that the cascade optimisation was able to reduce the computation time by 53% and still achieve a minimisation value 14% lower when compared to the YASA Vehicle Model Optimisation.
Keywords: Powertrain Optimisation; Battery Electric Vehicles; Machine Learning; Vehicle System Simulation; Design Parameters; Genetic Algorithm
College: College of Engineering
Funders: The authors would like to acknowledge the ASTUTE 2020 (Advanced Sustainable Manufacturing Technologies)operation supporting manufacturing companies across Wales, which has been part-funded by the European RegionalDevelopment Fund through the Welsh Government and the participating Higher Education Institutions.
Start Page: 592
End Page: 601