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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/160290 |
| _version_ | 1824456719598616576 |
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| author | Wu, Jingda Wei, Zhongbao Li, Weihan Wang, Yu Li, Yunwei Sauer, Dirk Uwe |
| 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 |
| author_sort | Wu, Jingda |
| collection | NTU |
| 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. |
| first_indexed | 2025-02-19T03:58:34Z |
| format | Journal Article |
| id | ntu-10356/160290 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:58:34Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-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. |
| 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 |
| title | 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_short | 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 |
| topic | Engineering::Electrical and electronic engineering Battery Health Energy Management |
| url | https://hdl.handle.net/10356/160290 |
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