 Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learningZheng Chen, Hengjie Hu, Yitao Wu, Yuanjian Zhang, Guang Li and Yonggang Liu Energy, 2020, vol. 211, issue C Abstract: 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. Keywords: Energy management strategy; Reinforcement learning; Markov chain; Velocity prediction; Stochastic model prediction control (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:211:y:2020:i:c:s0360544220320387 DOI: 10.1016/j.energy.2020.118931 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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