International Journal of Automotive Technology

, Volume 19, Issue 6, pp 1061–1069 | Cite as

New Optimal Power Management Strategy for Series Plug-In Hybrid Electric Vehicles

  • Erfan Taherzadeh
  • Shahram JavadiEmail author
  • Morteza Dabbaghjamanesh


Recently Plug-in hybrid electric vehicles (PHEVs) have gained increasing attention due to their ability to reduce the fuel consumption and emissions. In this paper a new efficient power management strategy is proposed for a series PHEV. According to the battery state of charge (SOC) and vehicle power requirement, a new rule-based optimal power controller with four different operating modes is designed to improve the fuel economy of the vehicle. Furthermore, the teaching-learning based optimization (TLBO) method is employed to find the optimal engine power and battery power under the specified driving cycle while the fuel consumption is considered as the fitness function. In order to demonstrate the effectiveness of the proposed method, four different driving cycles with various numbers of driving distances for each driving cycle are selected for the simulation study. The performance of the proposed optimal power management strategy is compared with the rule-based power management method. The results verify that the proposed power management method could significantly improve the fuel economy of the series PHEV for different driving conditions.

Key Words

Fuel economy Optimization Power management strategy Plug-in Hybrid Electric Vehicle (PHEV) Teaching-Learning Based Optimization (TLBO) 




power, W




torque, N.m


rotational speed, rpm


voltage, Volt


current, Ampere


force, N


resistance, Ohm


maximum capacity


vehicle mass, Kg


friction coefficient


air density, kg/m3


aerodynamic drag coefficient


vehicle velocity, m/s


frontal area, m2


road grade, rad


gravitational acceleration, m/s2









electric motor




final drive










open circuit




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Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Erfan Taherzadeh
    • 1
  • Shahram Javadi
    • 1
    Email author
  • Morteza Dabbaghjamanesh
    • 2
  1. 1.Intelligent Power System and Automation Research CenterIslamic Azad University, Central Tehran BranchTehranIran
  2. 2.Department of Electrical and Computer EngineeringLouisiana State UniversityBaton RougeUSA

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