Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization

Syuan Yi Chen, Yi Hsuan Hung, Chien Hsun Wu, Siang Ting Huang

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

This study developed an online suboptimal energy management system by using improved particle swarm optimization (IPSO) for engine/motor hybrid electric vehicles. The vehicle was modeled on the basis of second-order dynamics, and featured five major segments: a battery, a spark ignition engine, a lithium battery, transmission and vehicle dynamics, and a driver model. To manage the power distribution of dual power sources, the IPSO was equipped with three inputs (rotational speed, battery state-of-charge, and demanded torque) and one output (power split ratio). Five steps were developed for IPSO: (1) initialization; (2) determination of the fitness function; (3) selection and memorization; (4) modification of position and velocity; and (5) a stopping rule. Equivalent fuel consumption by the engine and motor was used as the fitness function with five particles, and the IPSO-based vehicle control unit was completed and integrated with the vehicle simulator. To quantify the energy improvement of IPSO, a four-mode rule-based control (system ready, motor only, engine only, and hybrid modes) was designed according to the engine efficiency and rotational speed. A three-loop Equivalent Consumption Minimization Strategy (ECMS) was coded as the best case. The simulation results revealed that IPSO searches the optimal solution more efficiently than conventional PSO does. In two standard driving cycles, ECE and FTP, the improvements in the equivalent fuel consumption and energy consumption compared to baseline were (24.25%, 45.27%) and (31.85%, 56.41%), respectively, for the IPSO. The CO2 emission for all five cases (pure engine, rule-based, PSO, IPSO, ECMS) was compared. These results verify that IPSO performs outstandingly when applied to manage hybrid energy. Hardware-in-the-loop (HIL) implementation and a real vehicle test will be conducted in the near future.

Original languageEnglish
Pages (from-to)132-145
Number of pages14
JournalApplied Energy
Volume160
DOIs
Publication statusPublished - 2015 Dec 15

Fingerprint

Powertrains
Energy management
Particle swarm optimization (PSO)
engine
Engines
fuel consumption
Fuel consumption
fitness
particle
energy management
electric vehicle
Energy management systems
Lithium batteries
lithium
torque
Hybrid vehicles
hardware
Internal combustion engines
simulator
energy

Keywords

  • Energy management
  • Hybrid vehicle
  • Online control
  • Particle swarm optimization (PSO)

ASJC Scopus subject areas

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

Cite this

Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization. / Chen, Syuan Yi; Hung, Yi Hsuan; Wu, Chien Hsun; Huang, Siang Ting.

In: Applied Energy, Vol. 160, 15.12.2015, p. 132-145.

Research output: Contribution to journalArticle

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abstract = "This study developed an online suboptimal energy management system by using improved particle swarm optimization (IPSO) for engine/motor hybrid electric vehicles. The vehicle was modeled on the basis of second-order dynamics, and featured five major segments: a battery, a spark ignition engine, a lithium battery, transmission and vehicle dynamics, and a driver model. To manage the power distribution of dual power sources, the IPSO was equipped with three inputs (rotational speed, battery state-of-charge, and demanded torque) and one output (power split ratio). Five steps were developed for IPSO: (1) initialization; (2) determination of the fitness function; (3) selection and memorization; (4) modification of position and velocity; and (5) a stopping rule. Equivalent fuel consumption by the engine and motor was used as the fitness function with five particles, and the IPSO-based vehicle control unit was completed and integrated with the vehicle simulator. To quantify the energy improvement of IPSO, a four-mode rule-based control (system ready, motor only, engine only, and hybrid modes) was designed according to the engine efficiency and rotational speed. A three-loop Equivalent Consumption Minimization Strategy (ECMS) was coded as the best case. The simulation results revealed that IPSO searches the optimal solution more efficiently than conventional PSO does. In two standard driving cycles, ECE and FTP, the improvements in the equivalent fuel consumption and energy consumption compared to baseline were (24.25{\%}, 45.27{\%}) and (31.85{\%}, 56.41{\%}), respectively, for the IPSO. The CO2 emission for all five cases (pure engine, rule-based, PSO, IPSO, ECMS) was compared. These results verify that IPSO performs outstandingly when applied to manage hybrid energy. Hardware-in-the-loop (HIL) implementation and a real vehicle test will be conducted in the near future.",
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