Optimizing EV Battery Charging Using Fuzzy Logic in the Presence of Uncertainties and Unknown Parameters

Ahmed, MU; Qays, MO; Lachowicz, S; Mahmud, P

Optimizing EV Battery Charging Using Fuzzy Logic in the Presence of Uncertainties and Unknown Parameters

Ahmed, MU Qays, MO Lachowicz, S Mahmud, P

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: ELECTRONICS, 2025, 15, (1)
Issue Date:: 2025-12-30

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Field	Value	Language
dc.contributor.author	Ahmed, MU
dc.contributor.author	Qays, MO
dc.contributor.author	Lachowicz, S
dc.contributor.author	Mahmud, P
dc.date.accessioned	2026-02-19T03:49:00Z
dc.date.available	2026-02-19T03:49:00Z
dc.date.issued	2025-12-30
dc.identifier.citation	ELECTRONICS, 2025, 15, (1)
dc.identifier.issn	2079-9292
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/193634
dc.description.abstract	The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	ELECTRONICS
dc.relation.isbasedon	10.3390/electronics15010177
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Optimizing EV Battery Charging Using Fuzzy Logic in the Presence of Uncertainties and Unknown Parameters
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Clean Energy Technology (CCET)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Clean Energy Technology (CCET)/Centre for Clean Energy Technology (CCET) Associate Members
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-02-19T03:48:58Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	1

Abstract:

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work.

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http://hdl.handle.net/10453/193634