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Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming

  • Zheng Chen
  • , Chris Chunting Mi*
  • , Rui Xiong
  • , Jun Xu
  • , Chenwen You
  • *Corresponding author for this work
  • University of Michigan, Dearborn

Research output: Contribution to journalArticlepeer-review

Abstract

This paper introduces an online and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV). Based on analytic analysis between fuel-rate and battery current at different driveline power and vehicle speed, quadratic equations are applied to simulate the relationship between battery current and vehicle fuel-rate. The power threshold at which engine is turned on is optimized by genetic algorithm (GA) based on vehicle fuel-rate, battery state of charge (SOC) and driveline power demand. The optimal battery current when the engine is on is calculated using quadratic programming (QP) method. The proposed algorithm can control the battery current effectively, which makes the engine work more efficiently and thus reduce the fuel-consumption. Moreover, the controller is still applicable when the battery is unhealthy. Numerical simulations validated the feasibility of the proposed controller.

Original languageEnglish
Pages (from-to)416-426
Number of pages11
JournalJournal of Power Sources
Volume248
DOIs
Publication statusPublished - 2014
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Fuel-rate
  • Genetic algorithm (GA)
  • Plug-in hybrid electric vehicle (PHEV)
  • Quadratic programming (QP)
  • State of charge (SOC)
  • State of health (SOH)

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