SDP-based extremum seeking energy management strategy for a power-split hybrid electric vehicle

Yu Wang, Zongxuan Sun

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

11 Scopus citations


The pursuit of high fuel efficiency and low emissions has inspired a lot of research efforts on automotive powertrain hybridization. Targeted at developing a real-time hybrid energy management strategy, a stochastic dynamic programming extremum seeking (SDP-ES) optimization algorithm with both the system states and output feedback is investigated in this paper. This SDP-ES algorithm utilizes a state-feedback control, which is offline generated by the stochastic dynamic programming (SDP), as a reference term to ensure the approximate global energy optimality and battery state-of-charge (SOC) sustainability. And in real-time, this algorithm injects a local feedback term via extremum seeking (ES), which is a non-model-based nonlinear optimization method, to compensate the control commands from the SDP and generate more fuel-efficient operation points along the specific SOC sustaining line, by leveraging the real-time measurement of system outputs (fuel consumption and emissions). The simulation results show the SDP-ES algorithm can provide desirable improvement of fuel economy based on the original SDP.

Original languageEnglish (US)
Title of host publication2012 American Control Conference, ACC 2012
Number of pages6
StatePublished - 2012
Event2012 American Control Conference, ACC 2012 - Montreal, QC, Canada
Duration: Jun 27 2012Jun 29 2012

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2012 American Control Conference, ACC 2012
CityMontreal, QC


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