Liu, TengHu, XiaosongLi, Shengbo EbenCao, Dongpu2017-11-102017-11-102017-08-14Teng Liu, Xiaosong Hu, Shengbo Eben Li and Dongpu Cao. Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle. IEEE/ASME Transactions on Mechatronics, Volume 22, Issue 4, August 2017, pp1497-15071083-4435http://dx.doi.org/10.1109/TMECH.2017.2707338https://dspace.lib.cranfield.ac.uk/handle/1826/12712This 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.enAttribution-NonCommercial 4.0 InternationalEnergy managementhybrid electric vehicle (HEV)Markov chain (MC)predictive controlreinforcement learning (RL)Article