Optimizing fuel economy of hybrid electric vehicles using a Markov decision process model

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

Research output: Chapter in Book/Entry/PoemConference contribution

8 Scopus citations


In contrast to conventional internal combustion engine (ICE) propelled vehicles, hybrid electric vehicles (HEVs) can achieve both higher fuel economy and lower pollutant emissions. The HEV features a hybrid propulsion system consisting of one ICE and one or more electric motors (EMs). The use of both ICE and EM increases the complexity of HEV power management, and so advanced power management policy is required for achieving higher performance and lower fuel consumption. This work aims at minimizing the HEV fuel consumption over any driving cycles, about which no complete information is available to the HEV controller in advance. Therefore, this work proposes to model the HEV power management problem as a Markov decision process (MDP) and derives the optimal power management policy using the policy iteration technique. Simulation results over real-world and testing driving cycles demonstrate that the proposed optimal power management policy improves HEV fuel economy by 23.9% on average compared to the rule-based policy.

Original languageEnglish (US)
Title of host publicationIV 2015 - 2015 IEEE Intelligent Vehicles Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781467372664
StatePublished - Aug 26 2015
EventIEEE Intelligent Vehicles Symposium, IV 2015 - Seoul, Korea, Republic of
Duration: Jun 28 2015Jul 1 2015

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings


OtherIEEE Intelligent Vehicles Symposium, IV 2015
Country/TerritoryKorea, Republic of

ASJC Scopus subject areas

  • Automotive Engineering
  • Computer Science Applications
  • Modeling and Simulation


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