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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) · hlm. 1025–1035

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

Abstrak

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.

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  • KIIT University
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  • Sri Venkateswara College of Engineering Tirupati
  • Mohan Babu University
  • Centurion University of Technology and Management
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Chinmoy Kumar PanigrahiKIIT University1
Sthitprajna MishraKIIT University1
Subhra DebdasKIIT University1
Atri BandyopadhyayKIIT University1
Amit Kumar SahooCenturion University of Technology and Management1
Pabitra Kumar TripathyAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<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>
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	<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>