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Hierarchical Optimization Control Strategy for Plug-in Hybrid Electric Vehicles Queue Based on Stochastic Dynamic Programming
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Lanxin Zhu1, Changdeng Zhou1, Jialun Cui2
Automobile Technology | 2023, (9) : 9 - 17
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Automobile Technology | 2023, (9): 9-17
Hierarchical Optimization Control Strategy for Plug-in Hybrid Electric Vehicles Queue Based on Stochastic Dynamic Programming
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Lanxin Zhu1, Changdeng Zhou1, Jialun Cui2
Affiliations
  • 1 Kunming University of Science and Technology, Kunming 650500
  • 2 Kunming Branch of the 705 Research Institute, China Shipbuilding Industry Corporation, Kunming 650101
Published: 2023-09-24 doi: 10.19620/j.cnki.1000-3703.20220213
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A hierarchical control strategy based on Radial Basis Function Neural Network (RBFNN) and Stochastic Dynamic Programming (SDP) was proposed for Plug-in Hybrid Electric Vehicle (PHEV) queue. Firstly, the powertrain structure and mathematical model of PHEV were analyzed in detail, then a hierarchical control framework was constructed. The upper layer adopted RBFNN to train driving data derived from Model Predictive Control (MPC) to generate the speed tracking controller. According to the information of the speed and power demand transmitted from the upper layer, a Markov chain model was established for the lower layer controller, the Markov chain model can realize the optimal energy distribution between PHEV traction battery and the engine based on the SDP theory. The simulation results show that compared with CD/CS strategy and rule-based strategy, the energy consumption of PHEVs in the queue is significantly reduced while ensuring safe driving under high-speed conditions based on the proposed strategy.

Hierarchical optimization  /  RBFNN  /  SDP  /  Markov chain
Lanxin Zhu, Changdeng Zhou, Jialun Cui. Hierarchical Optimization Control Strategy for Plug-in Hybrid Electric Vehicles Queue Based on Stochastic Dynamic Programming[J]. Automobile Technology, 2023 , (9) : 9 -17 . DOI: 10.19620/j.cnki.1000-3703.20220213
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doi: 10.19620/j.cnki.1000-3703.20220213
  • Online Date：2025-12-07
  • Published：2023-09-24
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  • Revised：2022-05-16
Affiliations
    1 Kunming University of Science and Technology, Kunming 650500
    2 Kunming Branch of the 705 Research Institute, China Shipbuilding Industry Corporation, Kunming 650101
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