Reinforcement learning based power management for hybrid electric vehicles

Xue Lin, Yanzhi Wang, Paul Bogdan, Naehyuck Chang, Massoud Pedram

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

14 Scopus citations

Abstract

Compared to conventional internal combustion engine (ICE) propelled vehicles, hybrid electric vehicles (HEVs) can achieve both higher fuel economy and lower pollution emissions. The HEV consists of a hybrid propulsion system containing one ICE and one or more electric motors (EMs). The use of both ICE and EM increases the complexity of HEV power management, and therefore requires advanced power management policies to achieve higher performance and lower fuel consumption. Towards this end, our work aims at minimizing the HEV fuel consumption over any driving cycle (without prior knowledge of the cycle) by using a reinforcement learning technique. This is in clear contrast to prior work, which requires deterministic or stochastic knowledge of the driving cycles. In addition, the proposed reinforcement learning technique enables us to (partially) avoid reliance on complex HEV modeling while coping with driver specific behaviors. To our knowledge, this is the first work that applies the reinforcement learning technique to the HEV power management problem. Simulation results over real-world and testing driving cycles demonstrate the proposed HEV power management policy can improve fuel economy by 42%.

Original languageEnglish (US)
Title of host publication2014 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2014 - Digest of Technical Papers
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages32-38
Number of pages7
EditionJanuary
ISBN (Electronic)9781479962785
DOIs
StatePublished - Jan 5 2015
Event2014 33rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2014 - San Jose, United States
Duration: Nov 2 2014Nov 6 2014

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
NumberJanuary
Volume2015-January
ISSN (Print)1092-3152

Other

Other2014 33rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2014
CountryUnited States
CitySan Jose
Period11/2/1411/6/14

Keywords

  • Hybrid electric vehicle (HEV)
  • power management
  • reinforcement learning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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    Lin, X., Wang, Y., Bogdan, P., Chang, N., & Pedram, M. (2015). Reinforcement learning based power management for hybrid electric vehicles. In 2014 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2014 - Digest of Technical Papers (January ed., pp. 32-38). [7001326] (IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD; Vol. 2015-January, No. January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCAD.2014.7001326