Optimisation methods for battery electric vehicle powertrain.
| dc.contributor.advisor | Auger, Daniel J. | |
| dc.contributor.advisor | Whidborne, James F. | |
| dc.contributor.author | Othaganont, Pongpun | |
| dc.date.accessioned | 2023-03-28T15:40:21Z | |
| dc.date.available | 2023-03-28T15:40:21Z | |
| dc.date.issued | 2017-07 | |
| dc.description.abstract | The battery electric vehicle (BEV) is considered to be one of the solutions for reducing greenhouse gasses and an alternative means of transportation. However, some current limitations such as higher powertrain costs, limited driving range and negative perceptions of that range, have reduced BEVs’ popularity. This thesis aims to improve the tank-to-wheel energy consumption of the BEV by presenting possible powertrain architectures and developing new tools for powertrain analysis. The study has two main objectives; the first is to evaluate different possible powertrain topologies. The selected topologies include the single-motor single-axle, the double-motor double-axle, the in-wheel-motor single-axle and the in-wheel-motor double-axle. Models of these powertrains have been modified from the Quasi-Static toolbox, using vehicle parameters from the Nissan Leaf and subject to state assumptions. The multi-objective optimisation method has been applied to establish the costs/benefits of energy consumption, acceleration performance and powertrain cost. The results show that each topology presents its own benefits as the in-wheel types are good at energy efficiency and drivability, while the cost of the powertrain is the major drawback. The non-in-wheel-motor vehicle provides sufficient energy efficiency and driveability with lower powertrain cost. The second objective is to evaluate a possible alternative tool for BEV powertrain modelling and optimisation. The tool consists of four methodologies: sensitivity analysis, differential flatness, the Chebfun computational tool and the multi-disciplinary optimisation method. The study presents a possible alternative optimisation tool which may perhaps benefit the designer. This new tool may not be as convenient as the previous one; however, the new tool may give the designer greater understanding and insight into the BEV powertrain. | en_UK |
| dc.description.coursename | PhD in Transport Systems | en_UK |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/19364 | |
| dc.language.iso | en | en_UK |
| dc.rights | © Cranfield University, 2015. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright holder. | |
| dc.subject | Battery electric vehicle | en_UK |
| dc.subject | multi-objective optimisation | en_UK |
| dc.subject | powertrain topologies | en_UK |
| dc.subject | sensitivity analysis | en_UK |
| dc.subject | differential flatness | en_UK |
| dc.subject | Chebfun | en_UK |
| dc.subject | multi-disciplinary optimisation | en_UK |
| dc.title | Optimisation methods for battery electric vehicle powertrain. | en_UK |
| dc.type | Thesis | en_UK |