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Online Power Management with Embedded Offline-Optimized Parameters for a Three-Source Hybrid Powertrain with an Experimental Emulation Application

Author

Listed:
  • Bedatri Moulik

    (Chair of Dynamics and Control, University of Duisburg-Essen, Lotharstr. 1, Duisburg 47057, Germany)

  • Dirk Söffker

    (Chair of Dynamics and Control, University of Duisburg-Essen, Lotharstr. 1, Duisburg 47057, Germany)

Abstract

Real-time power management in the presence of one or more reversible energy storage systems is a current issue with hybrid electric vehicles (HEVs). To evaluate the potentials of rule-based power management, optimization with respect to two conflicting objectives, fuel consumption and state of charge (SoC) deviation, is considered in this contribution. A modular structure of power management with decoupled offline and online parts is presented. The online part incorporates look-up tables (LUTs) with parameters from the offline optimization part. This permits an inclusion of more LUTs corresponding to different drive patterns. The goal of this contribution is to combine the real-time applicability of rule-based power management and the multi-objective optimization property of genetic algorithms in a single control strategy. Component aging problems are addressed by suitable design. The influence of sizing is investigated. Finally, an experimental setup consisting of components capable of realizing the dynamics of real powertrain components is realized and introduced. A verification/plausibility assessment of modeled dynamics based on the literature is considered. This newly-introduced concept represents a class of power management, which is easy to implement, can tackle different objectives in real time, and adapt itself to unknown driver demands.

Suggested Citation

  • Bedatri Moulik & Dirk Söffker, 2016. "Online Power Management with Embedded Offline-Optimized Parameters for a Three-Source Hybrid Powertrain with an Experimental Emulation Application," Energies, MDPI, vol. 9(6), pages 1-33, June.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:6:p:439-:d:71567
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    References listed on IDEAS

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    Cited by:

    1. Fang Zhou & Feng Xiao & Cheng Chang & Yulong Shao & Chuanxue Song, 2017. "Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-21, July.

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