System-Level Energy Management Optimization of Power-Split Hybrid Electric Vehicle Based on Nested Design

Sun, X; Dong, Z; Jin, Z; Lei, G; Tian, X

System-Level Energy Management Optimization of Power-Split Hybrid Electric Vehicle Based on Nested Design

Sun, X Dong, Z Jin, Z Lei, G

Tian, X

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Electronics, 2023, PP, (99)
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Sun, X
dc.contributor.author	Dong, Z
dc.contributor.author	Jin, Z
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Tian, X
dc.date.accessioned	2024-02-08T11:58:31Z
dc.date.available	2024-02-08T11:58:31Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Industrial Electronics, 2023, PP, (99)
dc.identifier.issn	0278-0046
dc.identifier.issn	1557-9948
dc.identifier.uri	http://hdl.handle.net/10453/175509
dc.description.abstract	The power-split hybrid electric vehicle (PS-HEV) is a complex and high-dimensional system that only through overall system-level optimization, considering the synergies and complementation between different components and subsystems, can achieve the best overall performance and benefits, which has been a technology challenge in the study. This article presents a system-level optimization method using nested design for engines and motors to solve the problem of the coupling between the parameters of the physical system and those of the control algorithm. Cohen's <italic>f</italic> effect is employed to conduct sensitivity analysis and determine the influence of the selected design parameters on the optimization objective. The Kriging model and NSGA II algorithm are proposed to optimize the motor, whereas the fire hawk algorithm is selected to optimize the fuel consumption objective. To evaluate the performance of the optimal torque distribution, a comparison is made with the original PS-HEV system, and the feasibility of the proposed scheme is verified through the hardware-in-the-loop test. The results demonstrate the efficacy of the proposed system-level optimization method in achieving superior performance of the PS-HEV system.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Industrial Electronics
dc.relation.isbasedon	10.1109/TIE.2023.3340212
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	System-Level Energy Management Optimization of Power-Split Hybrid Electric Vehicle Based on Nested Design
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-12-15T00:00:00+1000Z
dc.date.updated	2024-02-08T11:58:28Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

The power-split hybrid electric vehicle (PS-HEV) is a complex and high-dimensional system that only through overall system-level optimization, considering the synergies and complementation between different components and subsystems, can achieve the best overall performance and benefits, which has been a technology challenge in the study. This article presents a system-level optimization method using nested design for engines and motors to solve the problem of the coupling between the parameters of the physical system and those of the control algorithm. Cohen's f effect is employed to conduct sensitivity analysis and determine the influence of the selected design parameters on the optimization objective. The Kriging model and NSGA II algorithm are proposed to optimize the motor, whereas the fire hawk algorithm is selected to optimize the fuel consumption objective. To evaluate the performance of the optimal torque distribution, a comparison is made with the original PS-HEV system, and the feasibility of the proposed scheme is verified through the hardware-in-the-loop test. The results demonstrate the efficacy of the proposed system-level optimization method in achieving superior performance of the PS-HEV system.

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http://hdl.handle.net/10453/175509