Comparative study of different control strategies for plug-in hybrid electric vehicles

Vincenzo Marano, Pinak Tulpule, Stephanie Stockar, Simona Onori, Giorgio Rizzoni

Research output: Contribution to conferencePaperpeer-review

23 Scopus citations


Plug-In Hybrid Vehicles (PHEVs) represent the middle point between Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs), thus combining benefits of the two architectures. PHEVs can achieve very high fuel economy while preserving full functionality of hybrids - long driving range, easy refueling, lower emissions etc. These advantages come at an expense of added complexity in terms of available fuel. The PHEV battery is recharged both though regenerative braking and directly by the grid thus adding extra dimension to the control problem. Along with the minimization of the fuel consumption, the amount of electricity taken from the power grid should be also considered, therefore the electricity generation mix and price become additional parameters that should be included in the cost function. Two control algorithms - ECMS (Equivalent Consumption Minimization Strategy) and DP (dynamic programming) - are considered in this paper to optimize the power split between electrical and mechanical energy sources. The performance obtained using dynamic programming as global optimal energy management strategy for a PHEV is used as benchmark for evaluating on-board implementable control strategy - ECMS. The ECMS is used to design two control modes - EV and Blended. The model of a PHEV version of a Chevrolet Equinox fueled by bio-diesel B20 has been developed in the Matlab/Simulink environment. A Chevrolet Equinox was hybridized at The Center of Automotive Research (CAR), at The Ohio State University as part of Challenge-X competition; the vehicle was used to validate the components of the Simulink model.

Original languageEnglish (US)
StatePublished - Dec 1 2009
Event9th International Conference on Engines and Vehicles, ICE 2009 - Naples, Italy
Duration: Sep 13 2009Sep 13 2009


Other9th International Conference on Engines and Vehicles, ICE 2009

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering


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