Battery Health-Aware and Deep Reinforcement Learning-Based Energy Management for Naturalistic Data-Driven Driving Scenarios
This article proposes a battery health-aware and deep reinforcement learning (DRL)-based energy management framework for power-split hybrid electric vehicles (HEVs) in a naturalistic driving scenario. First, based on the data collected from the actual traffic flow, a data-driven method is used to establish driving scenarios that reflect different driving patterns and behaviors. Second, the expert knowledge is embedded into the deep deterministic policy gradient (DDPG) to achieve faster convergence with the guaranteed vehicle performance. Third, the superiority of the control strategy is achieved by optimizing the tradeoff among fuel consumption, battery aging cost, and state of charge (SoC) sustainability penalty under different weight coefficients, and verified by comparison with the existing state-of-the-art strategies including the deep Q-network (DQN) and dynamic programing (DP). The results show that the proposed strategy can slow down battery aging by lowering the operating severity factor with minimal fuel economy penalty while remaining accelerated iterative convergence compared with DQN. The benefits of proposed strategy become very evident when the vehicle is driving under the high power demand and it has good stability to cope with the change of operating conditions.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23327782
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Supplemental Notes:
- Copyright © 2022, IEEE.
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Authors:
- Tang, Xiaolin
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0000-0001-5709-291X
- Zhang, Jieming
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0000-0001-7262-4793
- Pi, Dawei
- Lin, Xianke
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0000-0001-5695-248X
- Grzesiak, Lech M
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0000-0002-2890-8163
- Hu, Xiaosong
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0000-0002-2769-4183
- Publication Date: 2022-3
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 948-964
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Serial:
- IEEE Transactions on Transportation Electrification
- Volume: 8
- Issue Number: 1
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 2332-7782
- Serial URL: http://ieeexplore.ieee.org/servlet/opac?punumber=6687316
Subject/Index Terms
- TRT Terms: Aging (Materials); Electric batteries; Electric vehicles; Hybrid vehicles; Machine learning; Optimization
- Subject Areas: Energy; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01846068
- Record Type: Publication
- Files: TRIS
- Created Date: May 23 2022 11:02AM