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World Electric Vehicle Journal is published by MDPI from Volume 9 issue 1 (2018). Articles in this Issue were published by The World Electric Vehicle Association (WEVA) and its member the European Association for e-Mobility (AVERE), the Electric Drive Transportation Association (EDTA), and the Electric Vehicle Association of Asia Pacific (EVAAP). They are hosted by MDPI on mdpi.com as a courtesy and upon agreement with AVERE.
Open AccessArticle

Plug-in Hybrid Electric Vehicle Energy Management System using Particle Swarm Optimization

1
Harpreetsingh Banvait, Graduate Student, IUPUI, Indianapolis, USA
2
Xiao Lin, PhD Student, Zhejiang University, Hangzhou, China
3
Sohel Anwar, Associate Professor, Dept of Mechanical Engineering, IUPUI, USA
4
Yaobin Chen, Professor and Chair of Dept of Electrical and Computer Engineering, IUPUI, USA
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2009, 3(3), 618-628; https://doi.org/10.3390/wevj3030618
Published: 25 September 2009
PDF [868 KB, uploaded 18 May 2018]

Abstract

Plug-in Hybrid Electric Vehicles (PHEVs) are the new generation of automobiles that can run not only on the energy from gasoline but also that from an electric outlet stored in a battery pack. Hence, these vehicles can significantly reduce the consumption of gasoline by taking advantage of cheaper renewable and non renewable sources of energies available at the domestic electric outlet. Thus PHEVs can contribute significantly in reducing the overall green house gas emissions from automobiles. In this paper a simplified powertrain of power split PHEV is modeled. The main objective of the study is to increase the fuel economy of the PHEV. To achieve this goal, a gradient free optimization algorithm, namely “Particle Swarm Optimization (PSO)” technique, has been implemented using the aforementioned simplified model. An optimization problem is formulated with Equivalent Fuel Consumption Minimization (EFCM) as the main objective function along with some constraints to be satisfied. This problem is then solved using the PSO algorithm and the optimal energy management algorithms are finally run in Argonne National Lab’s simulation software PSAT. The simulation results are then compared with PSAT’s default control strategy which indicate significant improvements in fuel economy with the PSO optimized algorithms.
Keywords: Plug-in Hybrid Electric Vehicle; Optimization; Particle Swarm Optimization Plug-in Hybrid Electric Vehicle; Optimization; Particle Swarm Optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Banvait, H.; Lin, X.; Anwar, S.; Chen, Y. Plug-in Hybrid Electric Vehicle Energy Management System using Particle Swarm Optimization. World Electr. Veh. J. 2009, 3, 618-628.

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