Hierarchical optimization control strategy of hybrid electric vehicle platoon under communication failure
Summary: This research proposes a DRL-based hierarchical optimization control strategy for connected HEV platoons through a cloud platform, addressing strong nonlinearity and communication failure issues. The strategy uses a signal-interference-plus-noise ratio model to detect network failures by co...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225009460 |
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| Summary: | Summary: This research proposes a DRL-based hierarchical optimization control strategy for connected HEV platoons through a cloud platform, addressing strong nonlinearity and communication failure issues. The strategy uses a signal-interference-plus-noise ratio model to detect network failures by considering distance, signal path loss, and wireless interference. The high-level control employs distributed model predictive control (DMPC) to generate desired commands for platoon driving during network failures. Meanwhile, the low-level control leverages prior knowledge of the engine’s optimal brake fuel consumption curve and battery characteristics to optimize energy management through knowledge and data fusion. To enhance energy planning efficiency, a PER-D2PG intelligent algorithm is introduced, integrating priority experience replay and dueling networks into DDPG. A trusted Markov decision process and a self-learning energy optimization framework are also established. Numerical results demonstrate that the proposed strategy effectively adjusts engine and motor power distribution, achieving vehicle car-following, safety, and energy-saving goals. |
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| ISSN: | 2589-0042 |