Journal
ENERGY
Volume 211, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.118931
Keywords
Energy management strategy; Reinforcement learning; Markov chain; Velocity prediction; Stochastic model prediction control
Categories
Funding
- National Key R&D Program of China [2018YFB0104000]
- National Natural Science Foundation of China [61763021, 51775063]
- EU [845102-HOEMEV-H2020-MSCA-IF-2018]
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In this paper, a stochastic model predictive control (MPC) method based on reinforcement learning is proposed for energy management of plug-in hybrid electric vehicles (PHEVs). Firstly, the power transfer of each component in a power-split PHEV is described in detail. Then an effective and convergent reinforcement learning controller is trained by the Q-learning algorithm according to the driving power distribution under multiple driving cycles. By constructing a multi-step Markov velocity prediction model, the reinforcement learning controller is embedded into the stochastic MPC controller to determine the optimal battery power in predicted time domain. Numerical simulation results verify that the proposed method achieves superior fuel economy that is close to that by stochastic dynamic programming method. In addition, the effective state of charge tracking in terms of different reference trajectories highlight that the proposed method is effective for online application requiring a fast calculation speed. (C) 2020 Elsevier Ltd. All rights reserved.
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