Title: Reinforcement Learning Optimized Look-Ahead Energy Management of a Parallel Hybrid Electric Vehicle
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| Authors: Liu, T; Hu, XS; Li, SE; Cao, DP
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| Author Full Names: Liu, Teng; Hu, Xiaosong; Li, Shengbo Eben; Cao, Dongpu
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| Source: IEEE-ASME TRANSACTIONS ON MECHATRONICS, 22 (4):1497-1507; 10.1109/TMECH.2017.2707338 AUG 2017
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| Language: English
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| Abstract: This paper presents a predictive energy management strategy for a parallel hybrid electric vehicle (HEV) based on velocity prediction and reinforcement learning (RL). The design procedure starts with modeling the parallel HEV as a systematic control-oriented model and defining a cost function. Fuzzy encoding and nearest neighbor approaches are proposed to achieve velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities of power demand. To determine the optimal control behaviors and power distribution between two energy sources, a novel RL-based energy management strategy is introduced. For comparison purposes, the two velocity prediction processes are examined by RL using the same realistic driving cycle. The look-ahead energy management strategy is contrasted with shortsighted and dynamic programming based counterparts, and further validated by hardware-in-the-loop test. The results demonstrate that the RL-optimized control is able to significantly reduce fuel consumption and computational time.
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| ISSN: 1083-4435
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| eISSN: 1941-014X
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| IDS Number: FE3UX
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| Unique ID: WOS:000408142300002
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