Adaptive real-time optimal control for energy management strategy of extended range electric vehicle

Yao, M; Zhu, B; Zhang, N

Adaptive real-time optimal control for energy management strategy of extended range electric vehicle

Yao, M Zhu, B Zhang, N

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Energy Conversion and Management, 2021, 234
Issue Date:: 2021-04-15

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Field	Value	Language
dc.contributor.author	Yao, M
dc.contributor.author	Zhu, B
dc.contributor.author	Zhang, N https://orcid.org/0000-0001-6408-0044
dc.date.accessioned	2022-03-09T05:49:45Z
dc.date.available	2022-03-09T05:49:45Z
dc.date.issued	2021-04-15
dc.identifier.citation	Energy Conversion and Management, 2021, 234
dc.identifier.issn	0196-8904
dc.identifier.issn	1879-2227
dc.identifier.uri	http://hdl.handle.net/10453/155101
dc.description.abstract	The equivalent fuel consumption minimization strategy (ECMS), as an instantaneous optimization energy management strategy (EMS), is one of the method used to realize real-time optimal control for extended range electric vehicles (EREVs). However, owing to the complexity of the fuel consumption and battery consumption models, the fuel economy optimization problem of EREVs is nonlinear and non-convex; thus, an equivalent factor (EF), i.e., the key parameter of ECMS, can only be solved by numerical iteration methods, such as the shooting method. This paper rewrites the output and state equations of the optimization problem by polynomial fitting and variable substitution of fuel and battery consumption models, transforms the fuel economy problem of an EREV into convex optimization, proposes a quadratic programming-based analytical solution method to solve EF, and provides an adaptive updating rule for terminal SOC to correct the influence of the prediction error of the driving cycle. Thereafter, an adaptive ECMS (A-ECMS) for the real-time optimal control of an EREV is presented and verified. Simulation results show that the proposed A-ECMS can maintain a terminal SOC close to the target value, and in terms of fuel economy, the difference between the proposed A-ECMS and dynamic programming-based global optimization EMS is less than 2%. Compared with the shooting method based on A-ECMS, the proposed A-ECMS achieves better results in all the performance of state of charge, including maintenance, fuel economy, and real-time performances. Especially for real-time performance, the mean computational efficiency of the proposed method is 13–23 times higher than that of the shooting method.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Energy Conversion and Management
dc.relation.isbasedon	10.1016/j.enconman.2021.113874
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Energy
dc.title	Adaptive real-time optimal control for energy management strategy of extended range electric vehicle
dc.type	Journal Article
utslib.citation.volume	234
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical 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 Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-03-09T05:49:43Z
pubs.publication-status	Published
pubs.volume	234

Abstract:

The equivalent fuel consumption minimization strategy (ECMS), as an instantaneous optimization energy management strategy (EMS), is one of the method used to realize real-time optimal control for extended range electric vehicles (EREVs). However, owing to the complexity of the fuel consumption and battery consumption models, the fuel economy optimization problem of EREVs is nonlinear and non-convex; thus, an equivalent factor (EF), i.e., the key parameter of ECMS, can only be solved by numerical iteration methods, such as the shooting method. This paper rewrites the output and state equations of the optimization problem by polynomial fitting and variable substitution of fuel and battery consumption models, transforms the fuel economy problem of an EREV into convex optimization, proposes a quadratic programming-based analytical solution method to solve EF, and provides an adaptive updating rule for terminal SOC to correct the influence of the prediction error of the driving cycle. Thereafter, an adaptive ECMS (A-ECMS) for the real-time optimal control of an EREV is presented and verified. Simulation results show that the proposed A-ECMS can maintain a terminal SOC close to the target value, and in terms of fuel economy, the difference between the proposed A-ECMS and dynamic programming-based global optimization EMS is less than 2%. Compared with the shooting method based on A-ECMS, the proposed A-ECMS achieves better results in all the performance of state of charge, including maintenance, fuel economy, and real-time performances. Especially for real-time performance, the mean computational efficiency of the proposed method is 13–23 times higher than that of the shooting method.

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http://hdl.handle.net/10453/155101