Reinforcement Learning Optimized Look-Ahead Energy Management of a Parallel Hybrid Electric Vehicle

By Teng Liu, Xiaosong Hu, Shengbo Eben Li, Dongpu Cao. IEEE/ASME transactions on mechatronics, 2017

353 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/01dc8463d554dc5dfd35b89b52e1ffe95890b1f4

How researchers see this paper

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In Automotive Engineering

  1. Shaobo Xie
  2. Zongke Xin
  3. James Brighton
  4. Xiaosong Hu

In Electrical and Electronic Engineering

  1. Shaobo Xie
  2. Zongke Xin
  3. James Brighton
  4. Xiaosong Hu

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