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A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy a...

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Autores principales: Li, Shibo, Chu, Liang, Hu, Jincheng, Pu, Shilin, Li, Jihao, Hou, Zhuoran, Sun, Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919596/
https://www.ncbi.nlm.nih.gov/pubmed/36772231
http://dx.doi.org/10.3390/s23031192
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author Li, Shibo
Chu, Liang
Hu, Jincheng
Pu, Shilin
Li, Jihao
Hou, Zhuoran
Sun, Wen
author_facet Li, Shibo
Chu, Liang
Hu, Jincheng
Pu, Shilin
Li, Jihao
Hou, Zhuoran
Sun, Wen
author_sort Li, Shibo
collection PubMed
description The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.
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spelling pubmed-99195962023-02-12 A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs Li, Shibo Chu, Liang Hu, Jincheng Pu, Shilin Li, Jihao Hou, Zhuoran Sun, Wen Sensors (Basel) Article The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS. MDPI 2023-01-20 /pmc/articles/PMC9919596/ /pubmed/36772231 http://dx.doi.org/10.3390/s23031192 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Shibo
Chu, Liang
Hu, Jincheng
Pu, Shilin
Li, Jihao
Hou, Zhuoran
Sun, Wen
A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs
title A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs
title_full A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs
title_fullStr A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs
title_full_unstemmed A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs
title_short A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs
title_sort novel a-ecms energy management strategy based on dragonfly algorithm for plug-in fcevs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919596/
https://www.ncbi.nlm.nih.gov/pubmed/36772231
http://dx.doi.org/10.3390/s23031192
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