Melola Discovery

← Melola Discovery

Open access Journal SINTA 1

Multi-objective energy management optimization in electric vehicles using fuzzy logic and particle swarm optimization

International Journal of Power Electronics and Drive Systems · Vol. 17 No. 2 (2026) · pp. 1025–1035

DOI
10.11591/ijpeds.v17.i2.pp1025-1035
ISSN
2722-256X, 2088-8694
Published
2026-06-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This paper proposes a hybrid energy management system (EMS) for electric vehicles by integrating fuzzy logic control (FLC) with particle swarm optimization (PSO) to improve power-split decision-making under dynamic driving conditions. The FLC is designed using state of charge (SoC) and vehicle speed as input variables and power split as the output. A set of fuzzy rules defines the EMS behavior, while PSO is employed to fine-tune decisions by maximizing an efficiency objective function defined as the closeness of the power split to an ideal reference. The simulation is implemented in Python using Colab-compatible packages such as scikit-fuzzy, DEAP, and matplotlib, ensuring accessibility and reproducibility. A test grid covering 10 SoC levels (10-100%) and 10 speed levels (10-120 km/h) is used to evaluate the system. Visualization tools, including heatmaps, 3D surface plots, and contour plots, are employed to represent the EMS behavior. The PSO-enhanced system achieved a maximum efficiency of 98.2% at an optimized SoC of 61.7% and a speed of 53.6 km/h, outperforming standalone fuzzy logic control. Tabulated results and statistical summaries validate the effectiveness of the proposed system.

Keywords

Collaboration network of the authors

11 authors · 31 co-authorship links · 12 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • KIIT University
  • SR University
  • Sri Venkateswara College of Engineering Tirupati
  • Mohan Babu University
  • Centurion University of Technology and Management
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Chinmoy Kumar PanigrahiKIIT University1
Sthitprajna MishraKIIT University1
Subhra DebdasKIIT University1
Atri BandyopadhyayKIIT University1
Amit Kumar SahooCenturion University of Technology and Management1
Pabitra Kumar TripathyAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Multi-objective energy management optimization in electric vehicles using fuzzy logic and particle swarm optimization</dc:title>
	<dc:creator>Devi, V. Lakshmi</dc:creator>
	<dc:creator>Reddy, Damodhar</dc:creator>
	<dc:creator>Velpula, Srikanth</dc:creator>
	<dc:creator>Kumar, K.</dc:creator>
	<dc:creator>Avula, Basi Reddy</dc:creator>
	<dc:subject xml:lang="en-US">efficiency optimization; electric vehicles; energy management system; fuzzy logic control; particle swarm optimization</dc:subject>
	<dc:description xml:lang="en-US">This paper proposes a hybrid energy management system (EMS) for electric vehicles by integrating fuzzy logic control (FLC) with particle swarm optimization (PSO) to improve power-split decision-making under dynamic driving conditions. The FLC is designed using state of charge (SoC) and vehicle speed as input variables and power split as the output. A set of fuzzy rules defines the EMS behavior, while PSO is employed to fine-tune decisions by maximizing an efficiency objective function defined as the closeness of the power split to an ideal reference. The simulation is implemented in Python using Colab-compatible packages such as scikit-fuzzy, DEAP, and matplotlib, ensuring accessibility and reproducibility. A test grid covering 10 SoC levels (10-100%) and 10 speed levels (10-120 km/h) is used to evaluate the system. Visualization tools, including heatmaps, 3D surface plots, and contour plots, are employed to represent the EMS behavior. The PSO-enhanced system achieved a maximum efficiency of 98.2% at an optimized SoC of 61.7% and a speed of 53.6 km/h, outperforming standalone fuzzy logic control. Tabulated results and statistical summaries validate the effectiveness of the proposed system.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/24393</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v17.i2.pp1025-1035</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 17, No 2: June 2026; 1025-1035</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v17.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/24393/15235</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 V. Lakshmi Devi, Damodhar Reddy, Srikanth Velpula, K. Kumar, Basi Reddy Avula</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>