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. Index Terms-Energy management, hybrid electric vehicle (HEV), Markov chain (MC), predictive control, reinforcement learning (RL).
Teng et al. REF proposed a hybrid vehicle energy prediction management strategy based on the speed prediction and reinforcement learning (RL) parallelization and significantly reduced fuel consumption.
Reinforcement Learning Optimized Look-Ahead Energy Management of a Parallel Hybrid Electric Vehicle
IEEE/ASME Transactions on Mechatronics
IEEE/ASME Transactions on Mechatronics
Engineering