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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 Orcid Logo, 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 Orcid Logo, Johann Sienz Orcid Logo

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DOI (Published version): 10.1016/j.procs.2021.08.061

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
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URI: https://cronfa.swan.ac.uk/Record/cronfa58380
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We propose a cascade optimisation method that takes advantage of two different vehicle models: the proprietary YASA MATLAB&#xAE; 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. 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spelling 2021-11-03T16:28:43.3366636 v2 58380 2021-10-18 Cascade Optimisation of Battery Electric Vehicle Powertrains 7a053c668886b4642286baed36fdba90 0000-0001-6356-3538 Matheus Torquato Matheus Torquato true false 31fdeba4e76994bc72c5b8954389f8ab Kayal Lakshmanan Kayal Lakshmanan true false 7fead5851d72ae17b6936afd3ee4533c 0000-0003-2387-6876 Alex Williams Alex Williams true false 7e0feb96ca2d685180b495e8983f3940 0000-0002-8170-0468 Fawzi Belblidia Fawzi Belblidia true false b952b837f8a8447055210d209892b427 0000-0003-1624-1725 Ashraf Fahmy Abdo Ashraf Fahmy Abdo true false 17bf1dd287bff2cb01b53d98ceb28a31 0000-0003-3136-5718 Johann Sienz Johann Sienz true false 2021-10-18 SCS 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. Journal Article Procedia Computer Science 192 592 601 Elsevier BV 1877-0509 Powertrain Optimisation; Battery Electric Vehicles; Machine Learning; Vehicle System Simulation; Design Parameters; Genetic Algorithm 1 10 2021 2021-10-01 10.1016/j.procs.2021.08.061 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 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. 2021-11-03T16:28:43.3366636 2021-10-18T10:26:05.7096325 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Matheus Torquato 0000-0001-6356-3538 1 Kayal Lakshmanan 2 Natalia Narożańska 3 Ryan Potter 4 Alex Williams 0000-0003-2387-6876 5 Fawzi Belblidia 0000-0002-8170-0468 6 Ashraf Fahmy Abdo 0000-0003-1624-1725 7 Johann Sienz 0000-0003-3136-5718 8 58380__21200__2838e59ed7994cc28369547d1b953023.pdf 58380.pdf 2021-10-18T10:27:10.7815225 Output 2562914 application/pdf Version of Record true © 2021 The Authors. This is an open access article under the CC BY-NC-ND license true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Cascade Optimisation of Battery Electric Vehicle Powertrains
spellingShingle Cascade Optimisation of Battery Electric Vehicle Powertrains
Matheus Torquato
Kayal Lakshmanan
Alex Williams
Fawzi Belblidia
Ashraf Fahmy Abdo
Johann Sienz
title_short Cascade Optimisation of Battery Electric Vehicle Powertrains
title_full Cascade Optimisation of Battery Electric Vehicle Powertrains
title_fullStr Cascade Optimisation of Battery Electric Vehicle Powertrains
title_full_unstemmed Cascade Optimisation of Battery Electric Vehicle Powertrains
title_sort Cascade Optimisation of Battery Electric Vehicle Powertrains
author_id_str_mv 7a053c668886b4642286baed36fdba90
31fdeba4e76994bc72c5b8954389f8ab
7fead5851d72ae17b6936afd3ee4533c
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17bf1dd287bff2cb01b53d98ceb28a31
author_id_fullname_str_mv 7a053c668886b4642286baed36fdba90_***_Matheus Torquato
31fdeba4e76994bc72c5b8954389f8ab_***_Kayal Lakshmanan
7fead5851d72ae17b6936afd3ee4533c_***_Alex Williams
7e0feb96ca2d685180b495e8983f3940_***_Fawzi Belblidia
b952b837f8a8447055210d209892b427_***_Ashraf Fahmy Abdo
17bf1dd287bff2cb01b53d98ceb28a31_***_Johann Sienz
author Matheus Torquato
Kayal Lakshmanan
Alex Williams
Fawzi Belblidia
Ashraf Fahmy Abdo
Johann Sienz
author2 Matheus Torquato
Kayal Lakshmanan
Natalia Narożańska
Ryan Potter
Alex Williams
Fawzi Belblidia
Ashraf Fahmy Abdo
Johann Sienz
format Journal article
container_title Procedia Computer Science
container_volume 192
container_start_page 592
publishDate 2021
institution Swansea University
issn 1877-0509
doi_str_mv 10.1016/j.procs.2021.08.061
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description 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.
published_date 2021-10-01T04:14:52Z
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score 11.016235

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