Q-Learning-Based Supervisory Control Adaptability Investigation for Hybrid Electric Vehicles

Bin Xu, Xiaolin Tang, Xiaosong Hu, Xianke Lin, Huayi Li, Dhruvang Rathod, Zhe Wang

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

As one of adaptive optimal controls, the Q-learning based supervisory control for hybrid electric vehicle (HEV) energy management is rarely studied for its adaptability. In real-world driving scenarios, conditions such as vehicle loads, road conditions and traffic conditions may vary. If these changes occur and the vehicle supervisory control does not adapt to it, the resulting fuel economy may not be optimal. To our best knowledge, for the first time, the study investigates the adaptability of Q-learning based supervisory control for HEVs. A comprehensive analysis is presented for the adaptability interpretation with three varying factors: driving cycle, vehicle load condition, and road grade. A parallel HEV architecture is considered and Q-learning is used as the reinforcement learning algorithm to control the torque split between the engine and the electric motor. Model Predictive Control, Equivalent consumption minimization strategy and thermostatic control strategy are implemented for comparison. The Q-learning based supervisory control shows strong adaptability under different conditions, and it leads the fuel economy among four supervisory controls in all three varying conditions.

Original languageEnglish
Pages (from-to)6797-6806
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume23
Issue number7
DOIs
StatePublished - Jul 1 2022

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.

Keywords

  • Q-learning
  • Reinforcement learning
  • hybrid electric vehicle
  • real-time implementation
  • supervisory control

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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