A Comprehensive Study on Energy Management Strategy Design of an Extended-Range Electric Vehicle

Xiao, Boyi

A Comprehensive Study on Energy Management Strategy Design of an Extended-Range Electric Vehicle

Xiao, Boyi

Permalink

Publication Type:: Thesis
Issue Date:: 2022

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (513.57 kB)

Adobe PDF

Download thesisAdobe PDF (5.86 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Xiao, Boyi
dc.date.accessioned	2023-01-13T02:25:16Z
dc.date.available	2023-01-13T02:25:16Z
dc.date.issued	2022
dc.identifier.uri	http://hdl.handle.net/10453/164949
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	A comprehensive research on the development process of an extended range logistics vehicle (ERLV) is conducted in the automobile theory and vehicle control points of view. The research priority of the study consists of three parts: the background review and the vehicle structure design, the consumption and cost analysis and the innovation on its control strategy. Firstly, a comprehensive background investigation and literature review of the energy management strategy is discussed. Secondly, an extended range mathematical platform for a logistics van is proposed. It presents a thorough energy consumption and Total cost of ownership analysis for an ERLV. Dynamic programming (DP) algorithm is adopted in the energy management strategy optimization to reveal the optimal energy consumption. Thirdly, a novel auxiliary power unit charging strategy with multi-object optimization is proposed using Reinforcement learning algorithms on the ERLV platform to achieve high fuel conversion efficiency while maintaining battery charging health. The comparative results show that the Soft Actor-Critic (SAC) had a 36% faster convergence speed than a traditional algorithm while providing a smoother and more stable action space. The fuel consumption with SAC also outplays by around 3%, which achieves almost 90% of the DP results.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/164949/2/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	au.edu.uts.lib/ppc
dc.title	A Comprehensive Study on Energy Management Strategy Design of an Extended-Range Electric Vehicle	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

A comprehensive research on the development process of an extended range logistics vehicle (ERLV) is conducted in the automobile theory and vehicle control points of view. The research priority of the study consists of three parts: the background review and the vehicle structure design, the consumption and cost analysis and the innovation on its control strategy. Firstly, a comprehensive background investigation and literature review of the energy management strategy is discussed. Secondly, an extended range mathematical platform for a logistics van is proposed. It presents a thorough energy consumption and Total cost of ownership analysis for an ERLV. Dynamic programming (DP) algorithm is adopted in the energy management strategy optimization to reveal the optimal energy consumption. Thirdly, a novel auxiliary power unit charging strategy with multi-object optimization is proposed using Reinforcement learning algorithms on the ERLV platform to achieve high fuel conversion efficiency while maintaining battery charging health. The comparative results show that the Soft Actor-Critic (SAC) had a 36% faster convergence speed than a traditional algorithm while providing a smoother and more stable action space. The fuel consumption with SAC also outplays by around 3%, which achieves almost 90% of the DP results.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/164949