A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles

Pu Zhao, Yanzhi Wang, Naehyuck Chang, Qi Zhu, Xue Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Hybrid electric vehicles employ a hybrid propulsion system to combine the energy efficiency of electric motor and a long driving range of internal combustion engine, thereby achieving a higher fuel economy as well as convenience compared with conventional ICE vehicles. However, the relatively complicated powertrain structures of HEVs necessitate an effective power management policy to determine the power split between ICE and EM. In this work, we propose a deep reinforcement learning framework of the HEV power management with the aim of improving fuel economy. The DRL technique is comprised of an offline deep neural network construction phase and an online deep Q-learning phase. Unlike traditional reinforcement learning, DRL presents the capability of handling the high dimensional state and action space in the actual decision-making process, making it suitable for the HEV power management problem. Enabled by the DRL technique, the derived HEV power management policy is close to optimal, fully model-free, and independent of a prior knowledge of driving cycles. Simulation results based on actual vehicle setup over real-world and testing driving cycles demonstrate the effectiveness of the proposed framework on optimizing HEV fuel economy.

Original languageEnglish (US)
Title of host publicationASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages196-202
Number of pages7
ISBN (Electronic)9781509006021
DOIs
StatePublished - Feb 20 2018
Event23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018 - Jeju, Korea, Republic of
Duration: Jan 22 2018Jan 25 2018

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Volume2018-January

Other

Other23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018
CountryKorea, Republic of
CityJeju
Period1/22/181/25/18

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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    Zhao, P., Wang, Y., Chang, N., Zhu, Q., & Lin, X. (2018). A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles. In ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings (pp. 196-202). (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASPDAC.2018.8297305