Multi-step reinforcement learning for model-free predictive energy management of an electrified off-highway vehicle

Quan Zhou, Ji Li, Bin Shuai, Huw Williams, Yinglong He, Ziyang Li, Hongming Xu*, Fuwu Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
376 Downloads (Pure)


The energy management system of an electrified vehicle is one of the most important supervisory control systems which manages the use of on-board energy resources. This paper researches a ‘model-free’ predictive energy management system for a connected electrified off-highway vehicle. A new reinforcement learning algorithm with the capability of ‘multi-step’ learning is proposed to enable the all-life-long online optimisation of the energy management control policy. Three multi-step learning strategies (Sum-to-Terminal, Average-to-Neighbour Recurrent-to-Terminal) are researched for the first time. Hardware-in-the-loop tests are carried out to examine the control functionality for real application of the proposed ‘model-free’ method. The results show that the proposed method can continuously improve the vehicle's energy efficiency during the real-time hardware-in-the-loop test, which increased from the initial level of 34% to 44% after 5 h’ 35-step learning. Compared with a well-designed model-based predictive energy management control policy, the model-free predictive energy management method can increase the prediction horizon length by 71% (from 35 to 65 steps with 1 s interval in real-time computation) and can save energy by at least 7.8% for the same driving conditions.

Original languageEnglish
Article number113755
Number of pages12
JournalApplied Energy
Early online date28 Aug 2019
Publication statusPublished - 1 Dec 2019


  • Energy management
  • Hybrid electric vehicle
  • Markov decision problem
  • Model-free predictive control
  • Multi-step reinforcement learning

ASJC Scopus subject areas

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law


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