Battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic DRL algorithm

Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized...

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Main Authors: Wu, Jingda, Wei, Zhongbao, Li, Weihan, Wang, Yu, Li, Yunwei, Sauer, Dirk Uwe
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160290
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1602902022-07-19T01:26:21Z Battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic DRL algorithm Wu, Jingda Wei, Zhongbao Li, Weihan Wang, Yu Li, Yunwei Sauer, Dirk Uwe School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Battery Health Energy Management Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost. 2022-07-19T01:26:21Z 2022-07-19T01:26:21Z 2020 Journal Article Wu, J., Wei, Z., Li, W., Wang, Y., Li, Y. & Sauer, D. U. (2020). Battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic DRL algorithm. IEEE Transactions On Industrial Informatics, 17(6), 3751-3761. https://dx.doi.org/10.1109/TII.2020.3014599 1551-3203 https://hdl.handle.net/10356/160290 10.1109/TII.2020.3014599 2-s2.0-85090638472 6 17 3751 3761 en IEEE Transactions on Industrial Informatics © 2020 IEEE. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Battery Health
Energy Management
spellingShingle Engineering::Electrical and electronic engineering
Battery Health
Energy Management
Wu, Jingda
Wei, Zhongbao
Li, Weihan
Wang, Yu
Li, Yunwei
Sauer, Dirk Uwe
Battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic DRL algorithm
description Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Wu, Jingda
Wei, Zhongbao
Li, Weihan
Wang, Yu
Li, Yunwei
Sauer, Dirk Uwe
format Article
author Wu, Jingda
Wei, Zhongbao
Li, Weihan
Wang, Yu
Li, Yunwei
Sauer, Dirk Uwe
author_sort Wu, Jingda
title Battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic DRL algorithm
title_short Battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic DRL algorithm
title_full Battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic DRL algorithm
title_fullStr Battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic DRL algorithm
title_full_unstemmed Battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic DRL algorithm
title_sort battery thermal-and health-constrained energy management for hybrid electric bus based on soft actor-critic drl algorithm
publishDate 2022
url https://hdl.handle.net/10356/160290
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